JL United States 

Environmental Protection 
Agency 


T D 427 
. P86 
R47 
2008 
Copy 2 


FT MEPDE 
GenCol1 


Results of the Lake Michigan 
Mass Balance Project: 
Atrazine Modeling Report 


RESEARCH AND DEVELOPMENT 



! » 






EPA/600/R-08/111 
September 2008 



Results of the Lake Michigan Mass 

Balance Project: 

Atrazine 

Modeling Report 


Prepared for 

United States Environmental Protection Agency 
Great Lakes National Program Office 
77 West Jackson Boulevard 
Chicago, Illinois 60604 


Prepared by 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research Branch 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 


Kenneth R. Rygwelski, Editor 



Recycled/Recyclable 

Printed with vegetable-based ink on 
paper that contains a minimum of 
50% post-consumer fiber content 
processed chlorine free. 



Notice 


The information in this document has been obtained primarily through funding by the United States 
Environmental Protection Agency (USEPA) under the auspices of the Office of Research and Development 
(ORD) and by the Great Lakes National Program Office (GLNPO). The report has been subjected to the 
Agency’s peer and administrative review and it has been approved for publication as an USEPA document. 
Mention of trade names or commercial products does not constitute endorsement or recommendation for use. 


LC Control Number 



2008 397498 


















Foreword 


The Lake Michigan Mass Balance Project (LMMBP) was initiated by the United States Environmental 
Protection Agency (USEPA), Great Lakes National Program Office (GLNPO) to determine strategies for 
managing and remediating toxic chemicals in the lake basin. Within the ecosystem approach, the mass 
balance framework is considered the best means of accomplishing this objective, and GLNPO requested the 
assistance of the USEPA Office of Research and Development (ORD) to facilitate and produce mathematical 
models that account for the sources, sinks, transport, fate, and food chain bioaccumulation of certain 
chemicals. This approach has been used in the past and builds upon the modeling efforts that have occurred 
in the Assessment and Remediation of Contaminated Sediments (ARCS) Program and the lower Fox 
River/Green Bay Mass Balance Project. The feasibility of such studies and resultant alternative management 
options for contaminants in large rivers and a large embayment were demonstrated, and a logical extension 
to the entire Lake Michigan receiving water body and major tributaries was warranted. There were a large 
number of cooperators in this project, and by focusing federal, state, local, private, and academic efforts and 
resources on a common goal, much more was accomplished than if these entities acted independently. 

The project was conducted in conjunction with the Enhanced Monitoring Program, and the approach required 
that all monitoring and field research be coordinated and common methodologies used. Mathematical 
modelers were consulted during planning for sample design, parameters, and temporal and spatial sampling 
considerations. This yielded a consistent and reliable database of information that was accessible by project 
participants and the public. Data for the LMMBP were collected during 1994 and 1995 and have been 
compiled according to specified quality assurance/quality control (QA/QC) requirements, and other data 
assessments have been made for modeling purposes. 

The need to consider the environmental benefits and consequences of alternative remediation choices to 
protect and improve our environment continues to intensify as: 1) environmental problems become more 
complex; 2) the means to address and investigate problems become more technical, time-consuming, and 
expensive; and 3) the actual cost to implement action strategies has escalated. The integrated atrazine mass 
balance modeling results are presented in this document and can aid managers in establishing priorities for 
both lake-wide and local improvements. Primary goals of the modeling effort were to determine the 
persistence of atrazine and to forecast concentrations in Lake Michigan water. The capability of forecast 
modeling presented here is a salient feature of this approach directed toward providing multiple alternatives, 
which then can be examined through benefit-cost analyses. 

This report presents the current status and results of the atrazine modeling effort through 2005, and it fulfills 
documentation requirements as described in the Quality Assurance Plan for Modeling: The Lake Michigan 
Mass Balance Project. Of course, a model and modeling applications are never complete, and it is expected 
that further efforts will change some results, insights, and our understanding of Lake Michigan. These efforts 
require an investment of resources and time, and improvements with additional model run executions are 
measured in years. In the larger picture, the need for Agency modeling technologies continues to intensify, 
and the requirement for reduced uncertainty will lead to future improved generations of models. We have 



placed great emphasis on following guidance provided by the USEPA and other agencies in assuring that the 
scientific theory is implemented accurately and completely by model computer code and that best modeling 
practices have been instituted. The fundamental principles driving the atrazine models presented in this report 
have received scientific peer review using an interdisciplinary panel of scientists and experts. The purpose 
of the reviews was to ensure that decisions based on the modeling efforts are reliable and scientifically 
credible. 

This document is not intended to include all of the details and background required to understand the entire 
LMMBP. Rather, the reader should refer to the LMMBP Work Plan and other materials on the GLNPO web 
site and the Lake Michigan Mass Balance Modeling Quality Assurance Plan on the ORD-Grosse lie web site 
for further information. 


IV 



Abstract 


The Lake Michigan Mass Balance Project (LMMBP) was conducted to measure and model nutrients, atrazine, 
polychlorinated biphenyls (PCBs), frans-nonachlor, and mercury to gain a better understanding of the sources, 
sinks, transport, fate, and effects of these substances within the system and to aid managers in the 
environmental decision-making process for the Lake Michigan basin. The United States Environmental 
Protection Agency (USEPA) Office of Research and Development (ORD) was requested to conduct and 
facilitate modeling in cooperation with the USEPA Great Lakes National Program Office (GLNPO); the USEPA 
Region V; other federal agencies; the states of Michigan, Wisconsin, Illinois, and Indiana; the tribes; and the 
public and private sectors. 

This report focuses on the load sources and fate and transport modeling of atrazine only. In the Lake Michigan 
basin, atrazine is used primarily as a herbicide on corn crops. With the recent increase in corn acreage in the 
United States associated with biofuel (ethanol) production, increased loadings of atrazine to lakes and streams 
are expected. 

The atrazine modeling effort described in this report was supported by intensive sampling of the atmosphere, 
major tributaries, and water column during the 1994-1995 field years as well as by extensive quality assurance 
and database development. Using these data and historical data, loadings of atrazine to the lake were 
estimated for the tributaries and atmosphere. Multimedia, mass balance modeling frameworks were applied 
to examine primary source and loss categories and make various model forecasts for a variety of loading 
scenarios. A literature search revealed that atrazine sorption to particles is negligible. Hence, atrazine 
transport associated with settling, resuspension, and burial were determined to be negligible. This report 
focuses on the modeling practices applied and results for atrazine from the MICHTOX screening-level model 
and the higher-resolution LM2-Toxic and LM3-Atrazine models. 

The results of the LM2-Toxic system mass balance model show that the largest atrazine load to the lake is 
from the watershed. For the year 1994, it was estimated that 5,264 kg of atrazine were discharged to the lake 
via the tributaries. The second major load to the lake was from atmospheric wet deposition with a loading 
estimate of 2,493 kg. The greatest loss of atrazine from the lake was through transport to Lake Huron (2,546 
kg) via the Straits of Mackinac. Loss due to internal decay (1,662 kg) was the second largest loss mechanism. 
The total inventory of atrazine in the lake was determined to be 184,310 kg in 1994. In this large, cold northern 
lake, the model suggests that in situ atrazine decay is very slow (0.009/year). This translates into an estimated 
atrazine half-life of 77 years. Using the model to forecast alternative futures, a 35% load reduction, if 
implemented in January 1,2005, would have been needed in order to prevent atrazine concentrations from 
increasing further in the lake. If loadings and boundary conditions are assumed to be constant in the future, 
the model predicts that the lake will eventually reach a steady-state concentration of 66 ng/L in the year 2194. 

Our high-resolution model, LM3-Atrazine, was primarily used to evaluate environmental exposure 
concentrations of atrazine in 5 km x 5 km model cells receiving loadings from the major tributaries to the lake. 


v 



The model segment receiving loads from the St. Joseph River, associated with the largest tributary load of 
atrazine to the lake, ranged from winter concentrations of 37 ng/L to spring peaks of 100-350 ng/L. These 
predicted exposure concentrations in the lake are all below selected toxicological endpoints, including the most 
sensitive, phytoplankton primary production reduction. 

This synthetic lake-wide perspective is anticipated to aid lake managers in moving forward on prevention, 
remedial actions, and legislative priorities associated with Lake Michigan Lake-wide Management Plans. The 
models developed provide an in-depth understanding of atrazine transport and fate processes in this valuable 
freshwater resource. This abstract does not necessarily reflect USEPA policy. 


VI 



Tables of Contents 


Notice. jj 

Foreword. jjj 

Abstract . v 

Table of Contents. vii 

List of Figures. xii 

List of Tables. xvi 

Abbreviations. xviii 

Acknowledgments. xx 

Executive Summary . xxi 

Part 1 Introduction . 1 

Chapter 1 Project Overview. 1 

1.1.1 Background . 1 

1.1.2 Description. 2 

1.1.3 Scope. 3 

1.1.3.1 Modeled Pollutants. 3 

1.1.3.1.1 PCBs . 3 

1.1.3.1.2 frans-Nonachlor . 5 

1.1.3.1.3 Atrazine . 5 

1.1.3.1.4 Mercury . 5 

1.1.3.2 Other Measured Parameters. 6 

1.1.3.3 Measured Compartments. 7 

1.1.4 Objectives.. 8 

1.1.5 Design . 8 

1.1.5.1 Organization . 8 

1.1.5.2 Study Participants . 8 

1.1.5.3 Workgroups. 9 

1.1.5.4 Information Management. 9 

1.1.5.4.1 Data Reporting. 9 

1.1.5.4.2 Great Lakes Environmental Monitoring Database. 10 

1.1.5.4.3 Public Access to LMMBP Data. 11 

1.1.5.5 Quality Assurance Program. 11 

1.1.6 Project Documents and Products. 13 


VII 



































Chapter 2 General Information on the Herbicide Atrazine and Its Degradation 

Products. 15 

1.2.1 Background . 15 

1.2.2 Physical-Chemical Properties of Atrazine. 16 

1.2.3 Atrazine Degradation . 17 

1.2.3.1 Biotic Degradation in Surface Water. 17 

1.2.3.2 Abiotic Degradation in Surface Water. 19 

1.2.3.2.1 Hydrolysis. 19 

1.2.3.2.2 Photolysis. 19 

1.2.3.3 Atrazine Degradation in Soil.. 20 

Chapter 3 Atrazine Field Data Observations. 23 

1.3.1 Background . 23 

1.3.2 Atmospheric Components . 24 

1.3.2.1 Sampling and Analytical Methodology. 24 

1.3.2.2 Results. 25 

1.3.2.2.1 Atrazine in the Gas Phase Fraction. 25 

1.3.2.2.2 Atrazine in the Particulate Fraction. 25 

1.3.2.2.3 Atrazine and Degradation Products in Wet Deposition. 26 

1.3.3 Atrazine in Tributaries. 29 

1.3.3.1 Sampling and Analytical Methodology. 30 

1.3.3.2 Results. 30 

1.3.4 Atrazine in Lake Water. 31 

1.3.4.1 Sampling and Analytical Methodology. 31 

1.3.4.2 Results.. 31 

1.3.4.2.1 Spatial Variation. 31 

1.3.4.2.2 Seasonal Variation . 32 

Appendix 1.3.1 Information Management. 35 

A1.3.1.1 Overview of Information Management at the LLRS . 35 

A1.3.1.2 Summary. 37 

Chapter 4 Representativeness of the Lake Michigan Mass Balance Project (LMMBP) 

Years Relative to Lake Michigan’s Historic Record . 46 

1.4.1 Introduction . 46 

1.4.2 Ice Cover . 46 

1.4.3 Water and Air Temperatures . 47 

1.4.4 Lake Water Levels . 50 

1.4.5 Precipitation .. 50 

1.4.5.1 Annual Comparisons . 51 

1.4.5.2 Monthly Comparisons . 51 

1.4.6 Tributary Flows. 51 

1.4.7 Summary . 51 


VIII 









































Chapter 5 Atrazine Modeling Overview 


55 


1.5.1 Background . 55 

1.5.2 LMMBP Modeling Objectives. 55 

1.5.3 Historical Modeling. 56 

1.5.3.1 Completely-Mixed Lakes-ln-Series Model . 57 

1.5.3.2 MICHTOX . 57 

1.5.3.3 Green Bay Mass Balance Project. 57 

1.5.4 Resolution for the LMMBP Models. 58 

1.5.5 Models Developed and Applied . 59 

1.5.5.1 Lake Process Models . 60 

1.5.5.2 Hydrodynamics (POM) . 60 

1.5.6 Model Quality Assurance. 60 

1.5.7 Model Application and Computational Aspects. 61 

1.5.7.1 Annual Simulations . 61 

1.5.7.2 Long-Term Simulations . 61 

Part 2 Lake Michigan Mass Balance Project Atrazine Loadings to Lake Michigan. 63 

Chapter 1 Historical Atrazine Usage in the United States. 63 

2.1.1 Background . 63 

2.1.2 Total Annual Usage Estimates. 64 

2.1.3 Future Atrazine Use Estimates. 64 

Chapter 2 Estimation of Atrazine Tributary Loadings . 69 

2.2.1 Atrazine Tributary Load Estimates Utilizing County-Level Atrazine 

Application Data. 69 

2.2.1.1 County-Level Atrazine Application Data. 70 

2.2.1.2 The Watershed Export Percentage. 70 

2.2.1.3 Calculating the Atrazine Tributary Load. 71 

2.2.2 Estimating Atrazine Tributary Loads for Years When County-Level Atrazine 

Application Data Was Not Available. 74 

2.2.3 Atrazine Tributary Loads for MICHTOX and LM2-Atrazine. 75 

2.2.4 Atrazine Tributary Load Estimates for LM3-Atrazine . 76 

2.2.4.1 Tributary Sampling Program . 76 

2.2.4.2 Atrazine Load Estimation for Monitored Rivers Using the Stratified 

Beale Ratio Estimator (SBRE) Method. 77 

2.2.4.3 Atrazine Load Estimation for Unmonitored Watersheds. 78 

2.2.5 Comments on Atrazine Tributary Loading Estimates . 79 

Chapter 3 Estimation of Atrazine Loads in Wet Deposition (Precipitation). 81 

2.3.1 Atmospheric Components Considered in Modeling Atrazine in Lake Michigan. 81 

2.3.2 Atrazine Wet Deposition Load Estimates Based on Measured Fluxes in the 

Basin. 82 

2.3.3 Atrazine Wet Deposition and Tributary Loads for MICHTOX and LM2-Atrazine .... 83 


IX 






































Part 3 Lake Michigan Mass Balance Project Level 1 Model: MICHTOX-Atrazine. 86 

3.1 MICHTOX-Atrazine Executive Summary . 86 

3.2 MICHTOX-Atrazine Recommendations . 86 

3.3 Model Description. 86 

3.3.1 Model Overview . 86 

3.3.2 MICHTOX Model Segmentation and Circulation. 87 

3.4 MICHTOX Model Application to Lake Michigan . 88 

3.4.1 Screening Model Application. 88 

3.4.2 Enhanced Screening Model Application . 89 

3.4.2.1 Field Data. 89 

3.4.2.2 Model Assumptions and Calibration Procedures . 89 

3.4.2.3 Tributary Loadings. 90 

3.4.2.4 Atmospheric Loadings . 90 

3.4.2.5 Model Confirmation . 90 

3.4.2.6 Model Application (Scenarios) . 90 

3.4.2.7 Discussion of Results. 91 

Part 4 Lake Michigan Mass Balance Project Level 2 Model: LM2-Atrazine. 95 

4.1 LM2-Atrazine Executive Summary. 95 

4.2 LM2-Atrazine Recommendations. 95 

4.3 Model Description . .. 95 

4.3.1 Model Overview . 95 

4.3.2 LM2-Atrazine Model Segmentation and Circulation. 96 

4.4 LM2-Atrazine Model Application to Lake Michigan. 98 

4.4.1 Enhanced Screening Model Application . 98 

4.4.2 Field Data. 98 

4.4.3 Tributary Loadings . 99 

4.4.4 Atmospheric Loadings. 99 

4.4.5 Model Assumptions. 99 

4.4.6 Model Calibration and Application (Scenarios) . 99 

4.4.7 Model Confirmation. 101 

4.4.8 Discussion of Results . 101 

Part 5 Lake Michigan Mass Balance Project Level 3 Model: LM3-Atrazine. 107 

5.1 LM3-Atrazine Executive Summary. 107 

5.2 LM3-Atrazine Recommendations. 108 

5.3 LM3-Atrazine Transport and Fate Modeling. 108 

5.3.1 Purpose of High-Resolution Model . 108 

5.3.2 Model Description and Framework. 109 

5.3.2.1 POM Hydrodynamic Model. 109 

5.3.2.2 Model Framework. 115 

5.3.2.2.1 Water Quality Processes. 115 

5.3.2.2.2 Spatial Resolution. 117 

5.3.2.2.3 Temporal Resolution. 118 

5.3.2.2.4 Model Assumptions. 118 


x 














































5.3.3 Description of Data Used . 118 

5.3.3.1 Field Data. 118 

5.3.3.2 Initial and Boundary Conditions. 118 

5.3.3.3 Loadings. 119 

5.3.3.3.1 Tributary . 119 

5.3.3.3.2 Atmospheric . 120 

5.3.4 Description of Model Simulations and Results . 121 

5.3.4.1 Mass Budgets . 124 

5.3.4.2 Selected Model Versus Observation Statistics. 124 

5.3.4.3 Comparison to Toxicological Endpoints . 124 

5.3.5 Model Uncertainty. 128 

Part 6 Review of Atrazine Models.'. 131 

6.1 LMMBP Atrazine Models . 131 

6.1.1 Peer Reviews of LMMBP Atrazine Models . 131 

6.1.2 Comparison of LMMBP Models. 132 

6.2 Comparison of LMMBP Models to Other Recent Atrazine Models Applied to 

Lake Michigan . 133 

6.2.1 Schottler and Eisenreich (1997) . 133 

6.2.2 Tierney et al. (1999) . 133 

6.3 Atrazine Models Applied to Lake or Deep River Systems Outside the Lake 

Michigan Basin. 134 

6.3.1 Swiss Lakes . 134 

6.3.2 St. Lawrence River. 135 

6.4 Atrazine Models Applied to Shallow Surface Water Systems in Agricultural 

Areas . 135 

6.4.1 Saylorville Reservoir, Iowa . 135 

6.4.2 Other Small Surface Water Systems. 136 

6.5 Conclusions . 136 

Appendix 6.1 Peer Review of LMMBP Atrazine Models, September 27, 2000, 

Romulus, Michigan. 138 

A6.1.1 Overview . 138 

A6.1.2 Comments on Technical Issues. 139 


XI 































List of Figures 


1.1.1 Simplified mass balance approach. 2 

1.1.2 The LMMBP sampling locations. 7 

1.1.3 Flow of information in the LMMBP. 10 

1.2.1 Chemical structures of atrazine and its major degradation products . 18 

1.3.1 Monthly precipitation amounts at cities in two large corn-growing regions. Data are 

from Peoria, Illinois and Omaha, Nebraska . 28 

1.3.2 Monthly average temperatures at cites in two large corn-growing regions. Data are 

from Peoria, Illinois and Omaha, Nebraska . 29 

1.3.3 Atrazine concentrations in Lake Michigan, 1994 .. . 32 

1.4.1 Location of the NOAA buoys in Lake Michigan. 49 

1.4.2 Monthly mean water temperatures in southern Lake Michigan. 49 

1.4.3 Monthly mean water temperatures in northern Lake Michigan. 49 

1.4.4 Mean June water temperatures in southern Lake Michigan. 49 

1.4.5 Mean June water temperatures in northern Lake Michigan . 49 

1.4.6 Monthly mean air temperatures in southern Lake Michigan. 50 

1.4.7 Monthly mean air temperatures in northern Lake Michigan . 50 

1.4.8 Mean June air temperatures in southern Lake Michigan . 50 

1.4.9 Mean June air temperatures in northern Lake Michigan . 50 

1.4.10 Record of mean monthly water levels for Lake Michigan. 51 

1.4.11 Annual precipitation to Lake Michigan between 1949 and 1998 .. 52 

xii 





















1.4.12 Comparison of 1982, 1983, 1994, and 1995 monthly mean precipitation to the mean 

for the period of 1949 through 1998 . 52 

1.4.13 Comparison of tributary flow for hydrodynamic model calibration (1982-1983) 

to the historic means . 53 

1.4.14 Comparison of tributary flow for the study period (1994-1995) to the historic means. 53 

1.5.1 Surface water segmentation for alternative Lake Michigan mass balance 

model levels. 58 

1.5.2 Model construct used for the LMMBP to model atrazine . 59 

2.1.1 Atrazine usage in the United States for 1991. 65 

2.1.2 Estimates of atrazine usage in the Lake Michigan basin for 1994 and 1995 . 66 

2.1.3 Historical trend of total annual usage of atrazine in the United States with acreage 

planted in corn, sorghum, and sugarcane . 66 

2.2.1 Soil textures typical for the Lake Michigan basin and part of the Lake Erie basin. 72 

2.2.2 WEP-based total atrazine tributary loading estimates to Lake Michigan . 75 

2.2.3 Tributary loadings to Lake Michigan MICHTOX model segments . 76 

2.2.4 WEP-based Lake Michigan tributary loadings, 1994 . 76 

2.2.5 1995 USGS SBRE atrazine loadings and median concentrations relative to median 

flow in Lake Michigan tributaries . 78 

2.3.1 Wet deposition (rain and snow) of atrazine for 1991 for Midwestern United States. 82 

2.3.2 Gradients of atrazine in wet deposition loadings over Lake Michigan for May 1994 . 83 

2.3.3 Seasonality of atrazine wet deposition loadings to Lake Michigan for 1994-1995 . 83 

2.3.4 Total atrazine tributary loading and wet deposition loading estimates to Lake Michigan .... 83 

2.3.5 Tributary and wet deposition loadings to MICHTOX model segments for 1994 and 

1995 . 84 

2.3.6 Tributary and wet deposition loadings to LM2-Atrazine model segments for 1994 and 

1995 . 84 

3.1 MICHTOX model segmentation. 87 

3.2 Total annual estimated tributary and precipitation loadings of atrazine to Lake Michigan .... 88 


XIII 























3.3 A comparison of MICHTOX - Predicted atrazine concentrations in Lake Michigan 

to averaged Lake Michigan data for the years 1991, 1992, and 1995 are depicted. 89 

3.4 Lake Michigan (open-lake) forecast scenarios: 1 - upper estimate of boundary 
condition, 2 - lower estimate of boundary condition, and 3 - estimate of average 

boundary condition . 92 

3.5 Lake Michigan (open-lake) hindcast and scenario forecasts: 4 - virtual elimination 

of all loadings and 0.0 ng/L atrazine at the Straits of Mackinac boundary, 5 - no tributary 
loads, 6 - no wet deposition, 7 - no further degradation of lake water quality. 92 

4.1 Water column segmentation for LM2-Atrazine. 97 

4.2 LM2-Atrazine model results for Lake Michigan and Green Bay for the year 1994 . 101 

4.3 LM2-Atrazine model runs of scenarios . 102 

4.4 Historical trends of United States corn acreage planted and harvested from 1986 

to 2007 . 103 

4.5 Model-predicted lake-wide averaged atrazine concentrations in water related to 

increases in atrazine loadings resulting from corn crop acreage increases are depicted ... 104 

5.1 Lake Michigan hydrodynamic model 5 km x 5 km computational grid. 110 

5.2 Simulated temperature (black) compared to measured temperature (gray) at two buoys 

in Lake Michigan for 1982-1983 . Ill 

5.3a Time-series of simulated water temperature versus observed at 45007 for 1994-1995 . 112 

5.3b Time-series of simulated surface water temperature versus observed at 45002 and 

45010 for 1994-1995 . 113 

5.4 Simulated mean temperature (°C) profile for 1982-1983 . 114 

5.5 Temporal evolution of simulated versus observed temperature profiles, Station 18M. 114 

5.6 Watershed and mid-lake sampling stations for the LMMBP study. 119 

5.7 Atrazine loads for Lake Michigan tributaries, 1994-1995 . 121 

5.8 Comparison of field data to predicted mid-lake surface concentrations for the 1994-1995 

model simulation and two loading conditions . 122 

5.9 Model simulation results of surface concentrations for May 29, 1995 using long-term 

WEP-based loads. 122 

5.10 Comparison of near-shore surface cell model results for the 1994-1995 model simulation 

and two loading conditions . 123 


XIV 






















5.11 Mid-lake surface concentration model results for 1994-2005 model simulation and two 

loading conditions. 125 

5.12 Mass budget average annual results for the 1994-1995 model simulations . 126 

5.13 Comparison of model predictions, measured data, and selected toxicological endpoints ... 127 


xv 





List of Tables 


1.1.1 Characteristics of the LMMBP Modeled Pollutants. 4 

1.1.2 The LMMBP Parameters. 6 

1.2.1 Physical and Chemical Properties of Atrazine . 17 

1.3.1 Summary of Wet Deposition Annual Volume-Weighted Mean Deethylatrazine (DEA) 
Concentrations, Atrazine Concentrations, and Deethylatrazine/Atrazine Ratios (DAR) for 

All Stations in the Lake Michigan Basin. 27 

1.3.2 Annual Mean Precipitation Amounts Measured at Chicago, Illinois; Fort Wayne, 

Indiana; South Bend, Indiana; Muskegon, Michigan; Grand Rapids, Michigan; and 

Milwaukee, Wisconsin . 29 

1.3.3 Summary of Historical Atrazine, DEA, and DIA Concentrations in Lake Michigan . 32 

A1.3.1 List of Parameters Analyzed and Principal Investigators for the LMMBP Atrazine 

Modeling . 36 

A1.3.2 Example of Data Verification Checklist Used for the LMMBP. 38 

A1.3.3 Printout of Information Stored in the LMMBP Tracking Database Related to Atrazine 

Modeling . 42 

A1.3.4 Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs . 43 

1.4.1 Summary of Lake Michigan Ice Cover Based Upon Assel (2003) . 48 

2.1.1. U.S. Department of Agriculture Corn Crop Summaries of Atrazine Usage in the 

United States for 1991, 1994, and 1995 . 65 

2.1.2 Total Annual Usage of Atrazine in the United States. 67 

2.2.1 Sources of County-Level Atrazine Application Data for the Lake Michigan Basin. 70 

2.2.2 Atrazine Watershed Export Data Summarized From the Literature . 72 

2.2.3 Atrazine Watershed Export Data From Various Northern Sites . 73 


XVI 



















5.1 1982-1983 Hydrodynamic Model Evaluations for Surface Temperature at NDBC 
Buoys (45002 and 45007) and Subsurface Temperature at GLERL Current Meter 

Moorings (28 Instruments) . 113 

5.2 1994-1995 Hydrodynamic Model Evaluations for Surface Temperature at NDBC 
Buoys (45002, 45007, and 45010) and Subsurface Temperature at GLERL Current 

Meter Moorings (10 Instruments). 113 

5.3 Mass Budget Average Annual Results for 1994-1995 Model Simulations . 125 

6.1 Comparison of LM2-Atrazine Model to Other Models. 134 


xvii 







f 


Abbreviations 


AOCs 

AREAL 

CMAQ 

CMC 

C0 2 

DAR 

DEA 

DIA 

DOC 

DQOs 

EMPs 

ERS 

EU 

FIFRA 

FQPA 

GBMBP 

GIS 

GLENDA 

GLERL 

GLNPO 

GLWQA 

GWP 

HUC 

IDLs 

IJC 

IRED 

LaMP 

LAPU 

LLRFRB 

LLRS 

LMMBP 

MCL 

MDEQ 

MDLs 

MED 

MQOs 

NDBC 

NHEERL 

NOAA 


Areas of Concern 

Atmospheric Research and Exposure Assessment Laboratory 

Community Multiscale Air Quality 

Criterion maximum concentration 

Carbon dioxide 

Deethylatrazine/atrazine ratio 

Deethylatrazine 

Deisopropylatrazine 

Dissolved organic carbon 

Data quality objectives 

Enhanced Monitoring Plans 

Economic Research Service 

European Union 

Federal Insecticide, Fungicide, and Rodenticide Act 

Food Quality Protection Act 

Green Bay Mass Balance Project 

Geographical Information System 

Great Lakes Environmental Monitoring Database 

Great Lakes Environmental Research Laboratory 

Great Lakes National Program Office 

Great Lakes Water Quality Agreement 

Great Waters Program 

Hydrological Unit Code 

Instrument detection limits 

International Joint Commission 

Interim Reregistration Eligibility Decision 

Lake-wide Management Plan 

Load as a percentage of use 

Large Lakes and Rivers Forecasting Research Branch 

Large Lakes Research Station 

Lake Michigan Mass Balance Project 

Maximum Contaminant Level 

Michigan Department of Environmental Quality 

Method detection limits 

Mid-Continent Ecology Division 

Measurement quality objectives 

National Data Buoy Center 

National Health and Environmental Effects Research Laboratory 
National Oceanic and Atmospheric Administration 


xviii 



ORD 

PCB 

PEM 

Pis 

POM 

QA 

QAPPs 

QC 

RAP 

RDMQ 

RED 

RMSD 

RPD 

SAP 

SBRE 

SCFAH 

SDLs 

Ti0 2 

TMDL 

USDA 

USDOI 

USEPA 

USFWS 

USGAO 

USGS 

VWA 

WEP 


Office of Research and Development 
Polychlorinated biphenyl 
Pesticide Emissions Model 
Principal Investigators 
Princeton Ocean Model 
Quality assurance 
Quality Assurance Project Plans 
Quality control 
Remedial Action Plan 

Research Data Management and Quality Control System 

Reregistration Eligibility Decision 

Root mean square difference 

Relative percent difference 

Scientific Advisory Panel 

Stratified Beale Ratio Estimator 

Standing Committee on the Food Chain and Animal Health 

System detection limits 

Titanium dioxide 

Total Maximum Daily Load 

United States Department of Agriculture 

United States Department of Interior 

United States Environmental Protection Agency 

United States Fish and Wildlife Service 

United States General Accounting Office 

United States Geological Survey 

Volume-weighted averages 

Watershed export percentage 


XIX 




Acknowledgments 


Special thanks to the United States Environmental Protection Agency, Great Lakes National Program Office 
for leadership, support, and collaboration on the Lake Michigan Mass Balance Project. The multiple efforts 
by the Principal Investigators for providing data, necessary for the modeling, are greatly appreciated. Thank 
you to Ronald Rossmann, Timothy Feist, James Pauer, Xiaomi Zhang, and Amy Anstead for providing 
valuable technical review comments. Thanks to Kay Morrison for the graphic renditions and figures and to 
Debra L. Caudill for formatting and word processing. Finally, thanks to Paul Capel, Miriam Diamond, Kevin 
Farley, Raymond Hoff, Robert Hudson, and Barry Lesht for serving on the peer-review panel. 


xx 



Executive Summary 


The Lake Michigan Mass Balance Project (LMMBP) provided an opportunity to improve our understanding of 
atrazine transport and fate in a large freshwater lake, Lake Michigan. A rigorous, quality-assured large 
supporting data set derived from samples collected in 1994-1995 was used to establish atmospheric and 
tributary loads, estimate initial conditions, and perform model calibration and confirmation exercises. Historical 
data collected outside of the LMMBP were also used to support the modeling effort. 

Models developed at the United States Environmental Protection Agency’s Large Lakes Research Station, 
to assess atrazine transport and fate in Lake Michigan included MICHTOX, LM2-Toxic, and LM3-Atrazine. 
Both LM2-Toxic and LM3-Atrazine utilized results from a hydrodynamic model to describe the lake’s physics. 
Results from air and tributary models were used to provide atrazine loadings to the lake. 

Lake Michigan is acted upon by a number of physical parameters that impact the hydrology, chemistry, and 
biology of the lake. For a lake the size of Lake Michigan, changes in these parameters can lead to significant 
changes, especially when models are used in long-term predictions to predict the outcome of various 
scenarios. The primary driving forces are wind, air temperature, and precipitation. These impact tributary 
flows, lake levels, waves, water circulation, water temperature, and ice cover. For the period of record, these 
driving forces vary from year-to-year. The period of 1982 to 1983 was used to calibrate the hydrodynamic 
models. For this period of time, hydrodynamic conditions were not at any extreme. This is also true for the 
period of 1994 and 1995 when the models were applied. 

Temperature will impact contaminant modeling. Air temperature impacts how quickly the lake warms in any 
one year. Water temperature impacts the volatilization of contaminants. There appears to be a four-year cycle 
of quicker warming which exists within a trend of general warming of the lake. The trend of warming may be 
part of a longer term, undocumented cycle, or may be related to climate change. 

MICHTOX is a toxic chemical mass balance and food chain bioaccumulation model developed in the early 
1990s. The model has nine water segments encompassing both Lake Michigan and Green Bay and is derived 
from the general water quality model WASP4. Before the onset of the LMMBP, MICHTOX was applied to Lake 
Michigan in a hindcast mode to gain an initial understanding of key atrazine processes in the lake and 
controlling loads. Tributary loadings of atrazine to the lake were determined based on historical usage of the 
chemical in the basin and a literature-derived Watershed Export Percentage (WEP) of 0.6%. The processes 
modeled included advection, dispersion, and reaction (decay). MICHTOX was used to provide a screening- 
level analysis of the potential future trends in atrazine concentrations in lake water under a variety of 
contaminant load scenarios. MICHTOX was run for seven scenarios to help evaluate the impacts on atrazine 
trends caused by various loading sources and boundary conditions. Results using the assumption of average 
boundary conditions indicate that atrazine decays at a rate of approximately 0.01/yr. This represents a half-life 
of atrazine in the lake due to decay of 69.3 years. MICHTOX modeling indicates that a total loading reduction 
of approximately 37%, if implemented on January 1,2005, would be needed to keep concentrations in the lake 
near steady-state. 


XXI 



LM2-Toxic is a sophisticated and state-of-the-art toxic chemical fate and transport model for Lake Michigan. 
LM2-Toxic is also a revision of the USEPA-supported WASP4 water quality modeling framework. The 
processes modeled included advection, dispersion, decay, absorption, and volatilization. The transport fields 
that were output from the 19-layered 5 km x 5 km gridded Princeton Ocean Model for the Great Lakes 
(POMGL) were aggregated and used by LM2-Toxic. The results of the LM2-Toxic system mass balance 
model show that the largest atrazine load to the lake is from the watershed. For the year 1994, it was 
estimated that 5,264 kg of atrazine were discharged to the lake via the tributaries. The second major load to 
the lake was from atmospheric wet deposition with a loading estimate of 2,493 kg. The greatest loss of 
atrazine from the lake was through transport to Lake Huron (2,546 kg) via the Straits of Mackinac. Loss due 
to internal decay (1,662 kg) was the second largest loss mechanism. The total inventory of atrazine in the lake 
was determined to be 184,310 kg in 1994. In this large, cold northern lake, the model suggests that in situ 
atrazine decay is very slow (0.009/year). This translates into an estimated atrazine half-life of 77 years. Using 
the model to forecast alternative futures, a 35% load reduction, if implemented in January 1,2005, would have 
been needed in order to prevent atrazine concentrations from increasing further in the lake. If loadings and 
boundary conditions are assumed to be constant in the future, the model predicts that the lake will eventually 
reach a steady-state concentration of 66 ng/L in the year 2194. 

LM3-Atrazine is a high-resolution (44,042 cells and 19 sigma layers) model that provides a better description 
of areas such as near and offshore zones, bays, river confluences, and the thermocline. The transport fields 
are provided by output from the Princeton Ocean hydrodynamics Model. Our high-resolution model, LM3- 
Atrazine, was primarily used to evaluate environmental exposure concentrations of atrazine in 5km x 5km 
model cells receiving loadings from the major tributaries to the lake. The modeled processes included 
advection, dispersion, decay, absorption, and volatilization. The atrazine decay (0.009/year) used in LM3- 
Atrazine was taken from the results derived from the hindcast run using LM2-Toxic. 

The model segment receiving loads from the St. Joseph River, associated with the largest tributary load of 
atrazine to the lake, ranged from winter concentrations of 37 ng/L to spring peaks of 100-350 ng/L. These 
predicted exposure concentrations in the lake are all below selected toxicological endpoints, including the most 
sensitive, phytoplankton primary production reduction. 

In comparing the results from the three LMMBP atrazine models to other models in the literature, it is apparent 
that atrazine decays very slowly in large lakes that stratify in the summer months. The literature suggests that 
degradation of atrazine in small lakes and streams that are well-mixed can be significant. A hypothesis can 
be formulated that the decay in surface water is likely to be dominated by photolytic processes, either directly 
or indirectly. In lakes that stratify in the summer, atrazine in the hypolimnion is isolated from the intense solar 
radiation during the peak time of the year. Hence, atrazine in this layer of the lake receives little degradation. 

The LMMBP atrazine models differ from two other atrazine models recently applied to Lake Michigan. The 
main reason for the differences appears to be based on how they estimated tributary loadings - both used 
higher estimates of tributary loadings. Consequently, these other models predicted much faster in situ decay. 
Since tributary loadings are the major source atrazine to the lake, detailed assessments of these loads is very 
important. 


XXII 




PART 1 


INTRODUCTION 


Chapter 1. Project Overview 

Harry B. McCarty, Ken Miller, Robert N. Brent, and 

Judy Schofield 

DynCorp (a CSC Company) 

601 Stevenson Avene 
Alexandria, Virginia 22304 
and 

Ronald Rossmann and Kenneth R. Rygwelski 
United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects 
Research Laboratory 
Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research 
Branch 

Large Lakes Research Station 

9311 Groh Road 

Grosse lie, Michigan 48138 

The United States Environmental Protection 
Agency’s (USEPA) Great Lakes National Program 
Office (GLNPO) instituted the Lake Michigan Mass 
Balance Project (LMMBP) to measure and model the 
concentrations of representative pollutants within 
important compartments of the Lake Michigan 
ecosystem. For the LMMBP, concentrations of 
polychlorinated biphenyls (PCBs), frans-nonachlor, 
and mercury were measured in tributaries, lake 
water, sediments, food webs, and the atmosphere 
surrounding Lake Michigan. Atrazine was measured 
only in the tributaries, lake water, and atmospheric 
components. This chapter provides an overview of 
the entire LMMBP. It includes a summary of the 
parameters measured and identifies the participants. 


Some of the data handling procedures are covered, 
as well as a listing of various project reports. 

1.1.1 Background 

The Great Lakes, which contain 20% of the world’s 
freshwater, are a globally important natural resource 
currently threatened by multiple stressors. While 
significant progress has been made to improve the 
quality of the lakes, pollutant loads from point, non¬ 
point, atmospheric, and legacy sources continue to 
impair ecosystem functions and limit the attainability 
of designated uses of these resources. Fish 
consumption advisories and beach closings continue 
to be issued, emphasizing the human health 
concerns from lake contamination. Physical and 
biological stressors, such as invasion of non-native 
species and habitat loss, also continue to threaten 
the biological integrity of the Great Lakes. 

The United States and Canada have recognized the 
significance and importance of the Great Lakes as a 
natural resource and have taken steps to restore and 
protect the lakes. In 1978, both countries signed the 
Great Lakes Water Quality Agreement (GLWQA). 
This Agreement calls for the restoration and 
maintenance of the chemical, physical, and biological 
integrity of the Great Lakes by developing plans to 
monitor and limit pollutant flows into the lakes. 

The GLWQA, as well as Section 118(c) of the Clean 
Water Act, require the development of a Lake-wide 
Management Plan (LaMP) for each Great Lake. The 
purpose of these LaMPs is to document an approach 
to reduce inputs of critical pollutants to the Great 
Lakes and restore and maintain Great Lakes 


1 



integrity. To assist in developing these LaMPs and 
to monitor progress in pollutant reduction, federal, 
state, tribal, and local entities have instituted 
Enhanced Monitoring Plans (EMPs). Monitoring is 
essential to the development of baseline conditions 
for the Great Lakes and provides a sound scientific 
base of information to guide future toxic load 
reduction efforts. 

The LMMBP is a part of the EMPs for Lake Michigan. 
The LMMBP was a coordinated effort among federal, 
state, and academic scientists to monitor tributary 
and atmospheric pollutant loads, develop source 
inventories of toxic substances, and evaluate the fate 
and effects of these pollutants in Lake Michigan. A 
mass balance modeling approach provides the 
predictive ability to determine the environmental 
benefits of specific load reduction scenarios for toxic 
substances and the time required to realize those 
benefits. This predictive ability will allow federal, 


state, tribal, and local agencies to make more 
informed load reduction decisions. 

1.1.2 Description 

The LMMBP used a mass balance approach to 
evaluate the sources, transport, and fate of 
contaminants in the Lake Michigan ecosystem. A 
mass balance approach is based on the law of 
conservation of mass, which states that the amount 
of a pollutant entering a system is equal to the 
amount of that pollutant leaving, trapped in, and 
chemically changed in the system (Figure 1.1.1). In 
the Lake Michigan system, pollutant inputs may 
come from atmospheric sources, adjacent lakes, or 
tributary loads. 

Pollutants may leave the system through burial in 
bottom sediments, volatilization to the atmosphere, or 
discharge into Lake Huron through the Straits of 


Simple Mass Budget for Conservative Substances 


source 


mass 


in 


water system 


mass Q U t = mass j n + ^sources 


source 


Mass Balance Modeling Approach 



air system 

A 


— air sources 

lass j|~| 

V 

water system 

mass q U { 

= mass , n + Xsources 


'4- 

± air-water exchange 


T 

sediment system 

± sediment-water exchange 
± linternal processes 


Figure 1.1.1. Simplified mass balance approach. 


2 


























Mackinac. The relative magnitude of these loss 
mechanisms is, in part, due to the physical and 
chemical properties of the chemicals being modeled. 
Pollutants within the system may be transformed 
through degradation or stored in ecosystem 
compartments such as the water column, sediments, 
or biota. 

For the LMMBP, contaminant concentrations in 
various inputs and ecosystem compartments over 
spatial and temporal scales were measured. 
Mathematical models that track the transport and fate 
of contaminants within Lake Michigan were 
developed and calibrated using these field data. The 
LMMBP models will serve as a basis for future mass 
budget/mass balance efforts for the LMMBP 
contaminants and other chemicals of interest. 

1.1.3 Scope 

1.1.3.1 Modeled Pollutants 

When the USEPA published the Water Quality 
Guidance for the Great Lakes System (58 FR 
20802), the Agency established water quality criteria 
for 29 pollutants. Those criteria were designed to 
protect aquatic life, terrestrial wildlife, and human 
health. PCBs, frans-nonachlor, and mercury are 
included in the list of 29 pollutants. The water quality 
criteria and values proposed in the guidance apply to 
all of the ambient waters of the Great Lakes system, 
regardless of the sources of pollutants in those 
waters. The proposed criteria provide a uniform 
basis for integrating federal, state, and tribal efforts 
to protect and restore the Great Lakes ecosystem. 

The number of pollutants that can be intensively 
monitored and modeled in the Great Lakes system is 
limited by the resources available to collect and 
analyze thousands of samples, assure the quality of 
the results, manage the data, and develop and 
calibrate the necessary models. Therefore, the 
LMMBP focused on constructing mass balance 
models for a limited group of pollutants. PCBs, frans- 
nonachlor, atrazine, and mercury were selected for 
inclusion in the LMMBP because these pollutants 
currently or potentially pose a risk to aquatic and 
terrestrial organisms (including humans) in the Lake 
Michigan ecosystem (Table 1.1.1). These pollutants 
also were selected to cover a wide range of chemical 


and physical properties and represent other classes 
of compounds which pose current or potential 
problems. Once a mass budget for selected 
pollutants is established and a mass balance model 
calibrated, additional contaminants can be modeled 
with limited data and future resources can be 
devoted to activities such as emission inventories 
and dispersion modeling. 

1.1.3.1.1 PCBs 

Polychlorinated biphenyls (PCBs) are a class of man¬ 
made, chlorinated, organic chemicals that include 
209 congeners, or specific PCB compounds. The 
highly stable, nonflammable, non-conductive 
properties of these compounds made them useful in 
a variety of products including electrical transformers 
and capacitors, plastics, rubber, paints, adhesives, 
and sealants. PCBs were produced for such 
industrial uses in the form of complex mixtures under 
the trade name “Aroclor” and were commercially 
available from 1930 through 1977, when the USEPA 
banned their production due to environmental and 
public health concerns. PCBs also may be produced 
by combustion processes, including incineration, and 
can be found in stack emissions and ash from 
incinerators. 

Because they were found by the USEPA in the 
effluents from one or more wastewater treatment 
facilities, seven Aroclor formulations were included in 
the Priority Pollutant List developed by the USEPA 
Office of Water under the auspices of the Clean 
Water Act. Aroclors may have entered the Great 
Lakes through other means, including spills or 
improper disposal of transformerfluids, contaminated 
soils washing into the watershed, or discharges from 
ships. The PCBs produced by combustion 
processes may be released to the atmosphere where 
they are transported in both vapor and particulate 
phases and enter the lakes through either dry 
deposition or precipitation events (e.g., rain). 

The stability and persistence of PCBs, which made 
them useful in industrial applications, have also made 
these compounds ubiquitous in the environment. 
PCBs do not readily degrade and thus accumulate in 
water bodies and aquatic sediments. PCBs also 
bioaccumulate, or build up, in living tissues. Levels 
of PCBs in some fish from Lake Michigan exceed 


3 



Table 1.1.1. Characteristics of the LMMBP Modeled Pollutants 


Pollutant 

Sources 

Uses 

Toxic Effects 

Biocon¬ 

centration 

Factor 1 

USEPA 

Regulatory 

Standards 2 

PCBs 

• Waste incinerators 
(unintentional 
byproducts of 
combustion) 

• Industrial 
dischargers 

• Electrical power 

• Electrical 
transformers and 
capacitors 

• Carbonless copy 
paper 

• Plasticizers 

• Hydraulic fluids 

• Probable human 
carcinogen 

• Hearing and vision 
impairment 

• Liver function alterations 

• Reproductive impairment 
and deformities in fish and 
wildlife 

1,800 to 
180,000 

MCL = 0.5 pg/L 
CCC = 14 ng/L 

HH = 0.17 ng/L 

frans-Non- 

achlor 3 

• Application to crops 
and gardens 

• Pesticide on corn 
and citrus crops 

• Pesticide on 
lawns and 
gardens 

• Probable human 
carcinogen 

• Nervous system effects 

• Blood system effects 

• Liver, kidney, heart, lung, 
spleen, and adrenal gland 
damage 

4,000 to 
40,000 

MCL = 2 pg/L 

CMC = 2.4 pg/L 
CCC = 4.3 ng/L 

HH = 2.1 ng/L 

Atrazine 

• Application to crops 

• Herbicide for corn 
and sorghum 
production 

• Weight loss 

• Cardiovascular damage 

• Muscle and adrenal 
degeneration 

• Congestion of heart, 
lungs, and kidneys 

• Toxic to aquatic plants 

2 to 100 

MCL = 3 pg/L 

CMC 4 = 350 
pg/L 

CCC 4 = 12 pg/L 

Mercury 

• Waste disposal 

• Manufacturing 
processes 

• Energy production 

• Ore processing 

• Municipal & medical 
waste incinerators 

• Chloralkali factories 

• Fuel combustion 

• Battery cells 

• Barometers 

• Dental fillings 

• Thermometers 

• Switches 

• Fluorescent lamps 

• Possible human 
carcinogen 

• Damage to brain and 
kidneys 

• Adverse affects on the 
developing fetus, sperm, 
and male reproductive 
organs 

63,000 to 
100,000 

MCL = 2 pg/L 

CMC = 1.4 pg/L 
CCC = 0.77 pg/L 
HH = 50 ng/L 

FWA 5 = 2.4 pg/L 
FWC 5 = 12 ng/L 
Wildlife 6 = 1.3 
ng/L 


'From: U.S. Environmental Protection Agency, 1995a, National Primary Drinking Water Regulations, Contaminant Specific 
Fact Sheets, Inorganic Chemicals, Technical Version, EPA 811/F-95/002-T, USEPA, Office of Water, Washington, D.C.; 
and U.S. Environmental Protection Agency, 1995b, National Primary Drinking Water Regulations, Contaminant Specific 
Fact Sheets, Synthetic Organic Chemicals, Technical Version, EPA 811/F-95/003-T, USEPA, Office of Water, 
Washington, D.C. 

2 MCL = Maximum Contaminant Level for drinking water. CMC = Criterion Maximum Concentration for protection of aquatic 
life from acute toxicity. CCC = Criterion Continuous Concentration for protection of aquatic life from chronic toxicity. HH 
= water quality criteria for protection of human health from water and fish consumption. Data from: U.S. Environmental 
Protection Agency, 1999, National Recommended Water Quality Criteria-Correction, EPA 822/Z-99/001, USEPA, Office 
of Water, Washington, D.C. 

Characteristics presented are for chlordane. trans -Nonachlor is a principal component of the pesticide chlordane. 

Craft water quality criteria for protection of aquatic life. From: U.S. Environmental Protection Agency, 2001b, Ambient 
Aquatic Life Water Quality Criteria for Atrazine, USEPA, Office of Water, Washington, D.C. 

5 FWA = Freshwater acute water quality criterion. FWC = Freshwater chronic water quality criterion. From National Toxics 
Rule (58 FR 60848). 

6 Wildlife criterion. From the Stay of Federal Water Quality Criteria for Metals (60 FR 22208), 40 CFR 131.36 and the 
Water Quality Guidance for the Great Lakes System (40 CFR 132). 


4 






the U.S. Food and Drug Administration tolerances, 
prompting closure of some commercial fisheries and 
issuance of fish consumption advisories. PCBs are 
a probable human carcinogen, and human health 
effects of PCB exposure include stomach, kidney, 
and liver damage; liver and biliary tract cancer; and 
reproductive effects, including effects on the fetus 
after exposure of the mother. 

PCB congeners exhibit a wide range of physical and 
chemical properties (e.g. vapor pressures, 
solubilities, boiling points), are relatively resistant to 
degradation, and are ubiquitous. These properties 
make them ideal surrogates for a wide range of 
organic compounds from anthropogenic sources. 

1.1.3.1.2 trans-Nonachlor 

trans -Nonachlor is a component of the pesticide 
chlordane. Chlordane is a mixture of chlorinated 
hydrocarbons that was manufactured and used as a 
pesticide from 1948 to 1988. Prior to 1983, 
approximately 3.6 million pounds of chlordane were 
used annually in the United States. In 1988, the 
USEPA banned all production and use of chlordane 
in the United States. 

Like PCBs, chlordane is relatively persistent and 
bioaccumulative. trans -Nonachlor is the most 
bioaccumulative of the chlordanes and is a probable 
human carcinogen. Other human health effects 
include neurological effects, blood dyscrasia, 
hepatoxicity, immunotoxicity, and endocrine system 
disruption. 

Historically, frans-nonachlor may have entered the 
Great Lakes through a variety of means related to 
the application of chlordane, including improper or 
indiscriminate application, improper cleaning and 
disposal of pesticide application equipment, or 
contaminated soil washing into the watershed. In the 
LMMBP, frans-nonachlor served as a model for the 
cyclodiene pesticides. 

1.1.3.1.3 Atrazine 

Atrazine is a triazine herbicide based on a ring 
structure with three carbon atoms alternating with 
three nitrogen atoms. Atrazine is the most widely 
used herbicide in the United States for corn and 


sorghum production. Atrazine has been used as an 
agricultural herbicide since 1959, and 64 to 75 million 
pounds of atrazine are used annually in the United 
States. Atrazine is extensively used in the upper 
Midwest, including the Lake Michigan watershed, 
where it is primarily associated with corn crops. 

Unlike PCBs and trans- nonachlor, atrazine is not 
bioaccumulative. It can be persistent in water; 
however, it is moderately susceptible to 
biodegradation in soils with a half-life of about 60-150 
days. Atrazine rarely exceeds the 3 ppb maximum 
contaminant level (MCL) set by the USEPA as a 
drinking water standard, but localized peak values 
can exceed the MCL following rainfall events after 
atrazine application. 

On January 31,2003, the USEPA issued an Interim 
Reregistration Eligibility Decision (IRED) for atrazine. 
In an October 2003 addendum to the IRED, the 
Agency concluded that there is sufficient evidence to 
formulate a hypothesis that atrazine exposure may 
impact gonadal development in amphibians, but 
there are currently insufficient data to either confirm 
or refute the hypothesis. However, in an October 
2007 report to the Federal Insecticide, Fungicide, and 
Rodenticide Act (FIFRA) Scientific Board, the 
Agency’s review concluded that the weight-of- 
evidence from a literature review does not show that 
atrazine produces consistent, reproducible effects 
across the range of exposure concentrations and 
amphibian species tested. Based on available test 
data, atrazine is not likely to be a human carcinogen. 
The Agency does have concern in regards to the 
potential hormonal effects observed in laboratory 
animals exposed to atrazine. Above certain 
concentration thresholds, atrazine is toxic to aquatic 
plants. In the LMMBP, atrazine served as a model to 
describe the transport and fate of a water soluble 
pesticide in current use. 

1.1.3.1.4 Mercury 

Mercury is a naturally-occurring toxic metal. Mercury 
is used in battery cells, barometers, thermometers, 
switches, fluorescent lamps, and as a catalyst in the 
oxidation of organic compounds. Global releases of 
mercury in the environment are both natural and 
anthropogenic (caused by human activity). It is 
estimated that about 11,000 metric tons of mercury 


5 



are released annually to the air, soil, and water from 
anthropogenic sources. These sources include 
combustion of various fuels such as coal; mining, 
smelting, and manufacturing activities; wastewater; 
and agricultural, animal, and food wastes. 

As an elemental metal, mercury is extremely 
persistent in all media. Mercury also bioaccumulates 
with reported bioconcentration factors in fish tissues 
in the range of 63,000 to 100,000. Mercury is a 
possible human carcinogen and causes the following 
human health effects: stomach, large intestine, 
brain, lung, and kidney damage; blood pressure and 
heart rate increase; and fetal damage. In the 
LMMBP, mercury served as a model for 
bioaccumulative metals. 

1.1.3.2 Other Measured Parameters 

In addition to the four chemicals modeled in the 
LMMBP, many other chemicals and parameters were 
measured in the LMMBP as part of the EMPs. A 
survey of these chemicals and parameters aids in the 
understanding of the overall ecological integrity of 
Lake Michigan. These additional parameters include 
various biological indicators; meteorological 
parameters; and organic, metal, and conventional 
chemicals in Lake Michigan. Many of the parameters 
included in this study are provided in Table 1.1.2. 


Table 1.1.2. The LMMBP Parameters 


Organics 


acenaphthene 

p,p’-DDT 

acenaphthylene 

endosulfan sulfate 

aldrin 

endosulfan 1 

anthracene 

endosulfan II 

atrazine 

endrin 

a-BHC 

endrin aldehyde 

3-BHC 

endrin ketone 

5-BHC 

fluoranthene 

y-BHC 

fluorene 

benzo[a]anthracene 

heptachlor 

benzo[g,/i,/]perylene 

heptachlor epoxide 

benzo[ib]fluoranthene 

hexachlorobenzene (HCB) 

benzof/cjfluoranthene 

indenofl ,2,3-cd]pyrene 

benzo[e]pyrene 

mirex 

benzofajpyrene 

trans- nonachlor 

a-chlordane 

oxychlordane 


Organics (Continued) 

benzo[a]pyrene 

trans-n onachlor 

a-chlordane 


oxychlordane 

y-chlordane 


PCBs congeners 

chrysene 


phenanthrene 

coronene 


pyrene 

p,p-DDE 


retene 

p,p’-DDD 


toxaphene 

Metals 

aluminum 


magnesium 

arsenic 


manganese 

calcium 


sodium 

cadmium 


nickel 

chromium 


lead 

cesium 


selenium 

copper 


thorium 

iron 


titanium 

mercury 


vanadium 

potassium 


zinc 


Conventionals 


alkalinity 

particulate organic carbon 

ammonia 

percent moisture 

bromine 

pH 

chloride 

phosphorus. 

chlorine 

silica 

sulfate 

silicon 

conductivity 

temperature 

dissolved organic 

total Kjeldahl nitrogen 

carbon 

total organic carbon 

dissolved oxygen 

total phosphorus 

dissolved phosphorus 

total suspended 

dissolved reactive silica 

particulates 

dry weight fraction 

ortho- phosphorus 

element carbon 

total hardness 

nitrate 

turbidity 

Biologicals 

fish species 

fish weight 

fish age 

fish length 

fish maturity 

fish taxonomy 

chlorophyll a 

fish diet analysis 

fish lipid amount 

primary productivity 

zooplankton 


Meteorological 

air temperature 

wind direction 

relative humidity 

wind speed 

barometric pressure 

visibility 

weather conditions 

wave height and direction * 


6 
















1.1.3.3 Measured Compartments 

In the LMMBP, contaminants were measured in the 

following compartments: 

• Open-Lake Water Column: The water column in 
the open-lake was sampled and analyzed for the 
modeled pollutants. 

• Tributaries: Major tributaries were sampled and 
analyzed for the modeled pollutants. 

• Fish: Top predators and forage base species 
were sampled and analyzed for diet analysis and 
contaminant burden. 

• Lower Pelagic Food Chain: Phytoplankton and 
zooplankton were sampled and analyzed for 
species diversity, taxonomy, and contaminant 
burden. 


• Sediments: Cores were collected and trap 
devices were used to collect sediment for 
determination of contaminants and sedimentation 
rates. 

• Atmosphere: Vapor, particulate, and precipitation 
phase samples were collected and analyzed for 
the modeled pollutants. 

For the modeled pollutants, more than 20,000 
samples were collected at more than 300 sampling 
locations and analyzed, including more than 9,000 
quality control (QC) samples (Figure 1.1.2). Field 
data collection activities were initially envisioned as 
a one-year effort. However, it became evident early 
into the project that a longer collection period would 
be necessary to provide a full year of concurrent 



Figure 1.1.2. The LMMBP sampling locations. 


7 











information on contaminant loads and ambient 
concentrations for modeling purposes. Therefore, 
field sampling occurred from April 1994 to October 
1995. 

1.1.4 Objectives 

The goal of the LMMBP was to develop a sound, 
scientific base of information to guide future toxic 
load reduction efforts at the federal, state, tribal, and 
local levels. To meet this goal, the four following 
LMMBP objectives were developed: 

► Estimate pollutant loading rates: Environmental 
sampling of major media will allow estimation of 
relative loading rates of critical pollutants to the 
Lake Michigan basin. 

► Establish baseline: Environmental sampling and 
estimated loading rates will establish a baseline 
against which future progress and contaminant 
reductions can be gauged. 

► Predict benefits associated with load 
reductions: The completed mass balance model 
will provide a predictive tool that environmental 
decision-makers and managers may use to 
evaluate the benefits of specific load reduction 
scenarios. 

► Understand ecosystem dynamics: Information 
from the extensive LMMBP monitoring and 
modeling efforts will improve our scientific 
understanding of the environmental processes 
governing contaminant cycling and availability 
within relatively closed ecosystems. 

1.1.5 Design 

1.1.5.1 Organization 

The GLNPO proposed a mass balance approach to 
provide coherent, ecosystem-based evaluation of 
toxics in Lake Michigan. GLNPO served as the 
program sponsor for the LMMBP. GLNPO formed 
two committees to coordinate study planning, the 
Program Steering Committee and the Technical 
Coordinating Committee.' These committees were 
comprised of federal, state, and academic 
laboratories as well as commercial laboratories (see 


Section 1.1.5.2, Study Participants). The committees 
administered a wide variety of tasks including: 
planning the project, locating the funding, designing 
the sample collection, coordinating sample collection 
activities, locating qualified laboratories, coordinating 
analytical activities, assembling the data, assuring 
the quality of the data, assembling skilled modelers, 
developing the models, and communicating interim 
and final project results. The Mid-Continent Ecology 
Division (MED) at Duluth, in cooperation with the 
National Oceanic and Atmospheric Administration 
(NOAA) Great Lakes Environmental Research 
Laboratory (GLERL) and the Atmospheric Sciences 
Modeling Division, supported the modeling 
component of the mass balance study by developing 
a suite of integrated mass balance models to 
simulate the transport, fate, and bioaccumulation of 
the study target analytes. 

1.1.5.2 Study Participants 

The LMMBP was a coordinated effort among federal, 
state, and academic scientists; and commercial 
laboratories. The following agencies and 
organizations have all played roles in ensuring the 
success of the LMMBP. Except for the three 
organizations indicated with an asterisk (*), all of the 
participants were members of the LMMBP Steering 
Committee. 

Federal and International 

► USEPA/GLNPO ( Program Sponsor) 

► USEPA/Region V Water Division (WD) 

► USEPA/Region V Air Division 

- USEPA/ORD/NHEERL/MED/LLRFRB 

► ORD/National Exposure Research Laboratory 

► U.S. Department of the Interior (USDOI) U.S. 
Geological Survey (USGS) Water Resources 
Division (WRD) 

► USDOI/USGS Biological Resources Division Great 
Lakes Science Center (GLSC) 

► U.S. Fish and Wildlife Service (USFWS) 

► U.S. Department of Energy 

► U.S. Department of Commerce NOAA/GLERL 

► USEPA/Office of Air and Radiation* 

► USEPA/Office of Water* 

► Environment Canada* 

► U.S. Department of Energy Battelle NW 


8 



State 

► Illinois Department of Natural Resources 

► Illinois Water Survey 

► Indiana Department of Environmental 
Management 

► Michigan Department of Natural Resources 

► Michigan Department of Environmental Quality 
(MDEQ) 

► Wisconsin Department of Natural Resources 

► Wisconsin State Lab of Hygiene 

Academic and Commercial 

► Indiana University 

► Rutgers University 

► University of Maryland 

► University of Michigan 

► University of Minnesota 

► University of Wisconsin 

► Grace Analytical 

1.1.5.3 Workgroups 

Eleven workgroups were formed to provide oversight 
and management of specific project elements. The 
workgroups facilitated planning and implementation 
of the study in a coordinated and systematic fashion. 
The workgroups communicated regularly through 
participation in monthly conference calls and annual 
“all-hands” meetings. Workgroup chairs were 
selected and were responsible for managing tasks 
under the purview of the workgroup and 
communicating the status of activities to other 
workgroups. The workgroups and workgroup chairs 
are listed below. 

• Program Steering Committee - Paul Horvatin 
(USEPA/GLNPO) 

• Technical Coordinating Committee-Paul Horvatin 
(USEPA/GLNPO) 

• Modeling Workgroup - William Richardson 
(USEPA/ORD/NHEERL7MED/LLRFRB) 

• Air Monitoring Workgroup - Jackie Bode (USEPA/ 
GLNPO) 

• Biota Workgroup - Paul Bertram (USEPA/ 
GLNPO) and John Gannon (USDOI/USGS/ 
GLSC) 

• Chemistry Workgroup - David Anderson (USEPA/ 
GLNPO) 


• Data Management Workgroup - Kenneth Klewin 
and Philip Strobel (USEPA/GLNPO) 

• Lake Monitoring Workgroup - Glenn Warren 
(USEPA/GLNPO) 

• Tributary Monitoring Workgroup - Gary Kohlhepp 
(USEPA/Region V/WD) and Robert Day (MDEQ) 

• Quality Assurance Workgroup - Louis Blume and 
Michael Papp (USEPA/GLNPO) 

• Sediment Monitoring Workgroup - Brian Eadie 
(NOAA/GLERL) 

1.1.5.4 Information Management 

As program sponsor, GLNPO managed information 
collected during the LMMBP. Principal Investigators 
(Pis) participating in the study reported field and 
analytical data to GLNPO. GLNPO developed a data 
standard for reporting field and analytical data and a 
database for storing and retrieving study data. 
GLNPO was also responsible for conducting data 
verification activities and releasing verified data to the 
study modelers and the public. The flow of 
information is illustrated in Figure 1.1.3. 

1.1.5.4.1 Data Reporting 

Over 20 organizations produced LMMBP data 
through the collection and analysis of more than 
20,000 samples. In the interest of standardization, 
specific formats (i.e., file formats and codes to 
represent certain data values) were established for 
reporting the LMMBP data. Each format specified 
the “rules” by which data were submitted, and, in 
many cases, the allowable values by which they were 
to be reported. The data reporting formats were 
designed to minimize the number of data elements 
reported from the field crews and laboratory analysis. 
Data reporting formats and the resulting Great Lakes 
Environmental Monitoring Database (GLENDA, see 
Sectionl .1.5.4.2) were designed to be applicable to 
projects outside the LMMBP as well. 

Principal Investigators (Pis) (including sampling 
crews and the analytical laboratories) supplied 
sample collection and analysis data following the 
standardized reporting formats, if possible. The 
LMMBP data were then processed through an 
automated SAS-based data verification system, 
Research Data Management and Quality Control 
System (RDMQ), for quality assurance (QA)/QC 


9 




Figure 1.1.3. Flow of information in the LMMBP. 


checking. After verification and validation by the Pis, 
the data sets were output in a form specific for 
upload to GLENDA. Finally, these data sets were 
uploaded to GLENDA. 

1.1.5.4.2 Great Lakes Environmental Monitoring 
Database 

Central to the data management effort is a 
computerized database system to house LMMBP 
and other project results. That system, GLENDA, 
was developed to provide data entry, storage, 
access, and analysis capabilities to meet the needs 
of mass balance modelers and other potential users 
of Great Lakes data. 


Development of GLENDA began in 1993 with a 
logical model based on the modernized STORET 
concept and requirements analysis. GLENDA was 
developed with the following guiding principles: 

• True multi-media scope: Water, air, sediment, 
taxonomy, fish tissue, fish diet, and meteorology 
data can all be housed in the database. 

• Data of documented quality: Data quality is 
documented by including results of quality control 
parameters. 

• Extensive contextual indicators: Ensure data 
longevity by including enough information to allow 
future or secondary users to make use of the data. 


10 










































• Flexible and expandable: Database is able to 
accept data from any Great Lakes monitoring 
project. 

• National compatibility: GLENDA is compatible 
with STORET and allows ease of transfer between 
these large databases. 

In an effort to reduce the data administration burden 
and ensure consistency of data in this database, 
GLNPO developed several key tools. Features 
including standard data definitions, reference tables, 
standard automated data entry applications, and 
analytical tools are (or will soon be) available. 

1.1.5.4.3 Public Access to LMMBP Data 

All LMMBP data that have been verified (through the 
QC process) and validated (accepted by the PI) are 
available to the public. Currently, GLNPO requires 
that written requests be made to obtain the LMMBP 
data. The data sets are available in several formats 
including WK1, DBF, and SD2. More information 
about the data sets is available on the LMMBP web 
site at: http://www.epa.gov/glnpo/lmmb/ 

database.html. 

The primary reason for requiring an official request 
form for the LMMBP data is to keep track of 
requests. This allows GLNPO to know how many 
requests have been made, who has requested data, 
and what use they intend for the data. This 
information assists GLNPO in managing and 
providing public access to Great Lakes data and 
conducting public outreach activities. As of 
November 2000, 38 requests for the LMMBP data 
have been made: eight from USEPA, five from other 
federal agencies, five from state agencies, five from 
universities, ten from consultants, three from 
international agencies, and two from non-profit or 
other groups. In the future, after all data are verified 
and validated, GLNPO intends to make condensed 
versions of the data sets available on the LMMBP 
web site for downloading. This will allow easy public 
access to the LMMBP data. 

Further information on the information management 
for the LMMBP can be found in The Lake Michigan 
Mass Balance Study Quality Assurance Report (U.S. 
Environmental Protection Agency, 2001a). 


1.1.5.5 Quality Assurance Program 

At the outset of the LMMBP, managers recognized 
that the data gathered and the models developed 
from the study would be used extensively by 
decision-makers responsible for making 
environmental, economic, and policy decisions. 
Environmental measurements are never true values 
and always contain some level of uncertainty. 
Decision-makers, therefore, must recognize and be 
sufficiently comfortable with the uncertainty 
associated with data on which their decisions are 
based. In recognition of this requirement, the 
LMMBP managers established a QA program goal of 
ensuring that data produced underthe LMMBP would 
meet defined standards of quality with a specified 
level of confidence. 

The QA program prescribed minimum standards to 
which all organizations collecting data were required 
to adhere. Data quality was defined, controlled, and 
assessed through activities implemented within 
various parameter groups (e.g., organic, inorganic, 
and biological parameters). QA activities included 
the following: 

► QA Program: Prior to initiating data collection 
activities, plans were developed, discussed, and 
refined to ensure that study objectives were 
adequately defined and to ensure that all QA 
activities necessary to meet study objectives were 
considered and implemented. 

► QA Workgroup: USEPA established a QA 
Workgroup whose primary function was to ensure 
that the overall QA goals of the study were met. 

► QA Project Plans (QAPPs): USEPA worked with 
Pis to define program objectives, data quality 
objectives (DQOs), and measurement quality 
objectives (MQOs) for use in preparing Quality 
Assurance Project Plans (QAPPs). Pis submitted 
QAPPs to the USEPA for review and approval. 
USEPA reviewed each QAPP for required QA 
elements and soundness of planned QA activities. 

► Training: Before beginning data collection 
activities, Pis conducted training sessions to 
ensure that individuals working on the project were 
capable of properly performing data collection 
activities for the LMMBP. 


11 



► Monthly Conference Calls and Annual 
Meetings: USEPA, Pis, and support contractors 
participated in monthly conference calls and 
annual meetings to discuss project status and 
objectives, QA issues, data reporting issues, and 
project schedules. 

► Standardized Data Reporting Format: Pis were 
required to submit all data in a standardized data 
reporting format that was designed to ensure 
consistency in reporting and facilitate data 
verification, data validation, and database 
development. 

► Intercomparison Studies: USEPA conducted 
studies to compare performance among different 
Pis analyzing similar samples. The studies were 
used to evaluate the comparability and accuracy 
of program data. 

► Technical Systems Audits: During the study, 
USEPA formally audited each Pi’s laboratory for 
compliance with their QAPPs, the overall study 
objectives, and pre-determined standards of good 
laboratory practice. 

► Data Verification: Pis and the USEPA evaluated 
project data against pre-determined MQOs and 
DQOs to ensure that only data of acceptable 
quality would be included in the program 
database. 

► Statistical Assessments: USEPA made 

statistical assessments of the LMMBP data to 
estimate elements of precision, bias, and 
uncertainty. 

► Data Validation: USEPA and modelers evaluated 
the data against the model objectives. 

Comparability of data among Pis participating in the 
LMMBP was deemed to be important for successful 
completion of the study. Therefore, MQOs for 
several data attributes were developed by the Pis 
and defined in the QAPPs. MQOs were designed to 
control various phases of the measurement process 
and to ensure that the total measurement uncertainty 
was within the ranges prescribed by the DQOs. 
MQOs were defined in terms of six attributes: 


► Sensitivity/Detectability: The determination of 
the low-range critical value that a method-specific 
procedure can reliably discern for a given 
pollutant. Sensitivity measures included, among 
others, method detection limits (MDLs) as defined 
at 40 CFR Part 136, system detection limits 
(SDLs), or instrument detection limits (IDLs). 

► Precision: A measure of the degree to which 
data generated from replicate or repetitive 
measurements differ from one another. Analysis 
of duplicate samples was used to assess 
precision. 

► Bias: The degree of agreement between a 
measured and actual value. Bias was expressed 
in terms of the recovery of an appropriate 
standard reference material or spiked sample. 

► Completeness: The measure of the number of 
samples successfully analyzed and reported 
compared to the number that were scheduled to 
be collected. 

► Comparability: The confidence with which one 
data set can be compared to other data sets. 

► Representativeness: The degree to which data 
accurately and precisely represent characteristics 
of a population, parameter variations at a 
sampling point, a process condition, or an 
environmental condition. 

The Pl-defined MQOs also were used as the basis 
for the data verification process. GLNPO conducted 
data verification through the LMMBP QA Workgroup. 
The workgroup was chaired by GLNPO’s QA 
Manager and consisted of QC Coordinators that were 
responsible for conducting review of specific data 
sets. Data verification was performed by comparing 
all field and QC sample results produced by each PI 
with their MQOs and with overall LMMBP objectives. 
If a result failed to meet predefined criteria, the QC 
Coordinator contacted the PI to discuss the result, 
verify that it was correctly reported, and determine if 
corrective actions were feasible. If the result was 
correctly reported and corrective actions were not 
feasible, the results were flagged to inform data 
users of the failure. These flags were not intended to 
suggest that data were not useable; rather they were 
intended to caution the user about an aspect of the 


12 



data that did not meet the predefined criteria. Data 
that met all predefined requirements were flagged to 
indicate that the results had been verified and were 
determined to meet applicable MQOs. In this way, 
every data point was assigned one or more validity 
flags based on the results of the QC checks. GLNPO 
also derived data quality assessments for each 
LMMBP data set for a subset of the attributes listed 
above, specifically sensitivity, precision, and bias. 
The LMMBP modelers and the LLRS Database 
Manager also performed data quality assessments 
prior to inputting data into study models. Such 
activities included verifying the readability of 
electronic files, identifying missing data, checking 
units, and identifying outliers. A detailed description 
of the QA program is included in The Lake Michigan 
Mass Balance Project Quality Assurance Report 
(U.S. Environmental Protection Agency, 2001a). A 
brief summary of quality implementation and 
assessment is provided in each of the following parts. 

1.1.6 Project Documents and Products 

During project planning, LMMBP participants 
developed study tools including work plans, a 
methods compendium, QAPPs, and data reporting 
standards. Through these tools, LMMBP participants 
documented many aspects of the study including 
information management and QA procedures. Many 
of these documents are available on GLNPO’s 
website at http://www.epa.gov/glnpo/lmmb. 

The LMMBP Work Plan 

Designers of the LMMBP have documented their 
approach in a report entitled Lake Michigan Mass 
Budget/Mass Balance Work Plan (U.S. 
Environmental Protection Agency, 1997a). The 
essential elements of a mass balance study and the 
approach used to measure and model these 
elements in the Lake Michigan system are described 
in the work plan. This document was developed 
based upon the efforts of many federal and state 
scientists and staff who participated in the initial 
planning workshop, as well as Pis. 

QA Program/Project Plans 

The Lake Michigan Mass Balance Project: Quality 
Assurance Plan for Mathematical Modeling, Version 


3.0 (Richardson et al., 2004) documents the QA 
process for the development and application of 
LMMBP models, including hydrodynamic, sediment 
transport, eutrophication, transport chemicalfate, and 
food chain bioaccumulation models. 

The Enhanced Monitoring Program QA Program 
Plan 

The Enhanced Monitoring Program Quality 
Assurance Program Plan (U.S. Environmental 
Protection Agency, 1997b) was developed in 1993 to 
ensure that data generated from the LMMBP 
supported its intended use. 


The Lake Michigan Mass Balance Project Methods 
Compendium (U.S. Environmental Protection 
Agency, 1997c, 1997d) describes the sampling and 
analytical methods used in the LMMBP. The entire 
three volumes are available on GLNPO’s website 


The LMMBP Data Reporting Formats and Data 
Administration Plan 

Data management for the LMMBP was a focus from 
the planning stage through data collection, 
verification, validation, reporting, and archiving. The 
goal of consistent and compatible data was a key to 
the success of the project. The goal was met 
primarily through the development of standard 
formats for reporting environmental data. The data 
management philosophy is outlined on the LMMBP 
website mentioned above. 


"Annex 2" of the 1972 Canadian-American Great 
Lakes Water Quality Agreement (amended in 1978, 
1983, and 1987) prompted development of a Lake¬ 
wide Area Management Plan (LaMP) for each Great 
Lake. The purpose of these LaMPs is to document 
an approach to reducing input of critical pollutants to 
the Great Lakes and restoring and maintaining Great 
Lakes integrity. The Lake Michigan LaMP calls for 
basin-wide management of toxic chemicals. 


The LMMBP Methods Compendium 


mentioned above. 


Lake Michigan LaMP 


13 



GLENDA Database 

Central to the data management effort is a 
computerized data system to house LMMBP and 
other project results. That system, the Great Lakes 
Environmental Monitoring Database (GLENDA), was 
developed to provide data entry, storage, access, 
and analysis capabilities to meet the needs of mass 
balance modelers and other potential users of Great 
Lakes data. 

References 

Richardson, W.L., D.D. Endicott, R.G. Kreis, Jr., and 
K.R. Rygwelski (Eds.). 2004. The Lake Michigan 
Mass Balance Project Quality Assurance Plan for 
Mathematical Modeling. Prepared by the 
Modeling Workgroup. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and Environmental 
Effects Research Laboratory, Mid-Continent 
Ecology Division, Large Lakes Research Station, 
Grosse lie, Michigan. EPA/600/R-04/018, 233 

pp. 

U.S. Environmental Protection Agency. 1995a. 
National Primary Drinking Water Regulations, 
Contaminant Specific Fact Sheets, Inorganic 
Chemicals, Technical Version. U.S. 
Environmental Protection Agency, Office of 
Water, Washington, D.C. EPA/811/F-95/002-T. 

U.S. Environmental Protection Agency. 1995b. 
National Primary Drinking Water Regulations, 
Contaminant Specific Fact Sheets, Synthetic 
Organic Chemicals, Technical Version. U.S. 
Environmental Protection Agency, Office of 
Water, Washington, D.C. EPA/811/F-95/003-T. 

U.S. Environmental Protection Agency. 1997a. Lake 
Michigan Mass Budget/Mass Balance Work Plan. 
U.S. Environmental Protection Agency, Great 
Lakes National Program Office, Chicago, Illinois. 
EPA/905/R-97/018, 155 pp. 


U.S. Environmental Protection Agency. 1997b. The 
Enhanced Monitoring Program Quality Assurance 
Program Plan. U.S. Environmental Protection 
Agency, Great Lakes National Program Office, 
Chicago, Illinois. EPA/905/R-97/017, 61 pp. 

U.S. Environmental Protection Agency. 1997c. Lake 
Michigan Mass Balance Study (LMMB) Methods 
Compendium, Volume 1: Sample Collection 
Techniques. U.S. Environmental Protection 
Agency, Great Lakes National Program Office, 
Chicago, Illinois. EPA/905/R-97/012a, 1,440 pp. 

U.S. Environmental Protection Agency. 1997d. Lake 
Michigan Mass Balance Study (LMMB) Methods 
Compendium, Volume 2: Organic and Mercury 
Sample Analysis Techniques. U.S. 
Environmental Protection Agency, Great Lakes 
National Program Office, Chicago, Illinois. 
EPA/905/R-97/012b, 532 pp. 

U.S. Environmental Protection Agency. 1999. 
National Recommended Water Quality Criteria- 
Correction. U.S. Environmental Protection 
Agency, Office of Water, Washington, D.C. 
EPA/822/Z-99/001,25 pp. 

U.S. Environmental Protection Agency. 2001a. The 
Lake Michigan Mass Balance Study Quality 
Assurance Report. U.S. Environmental 
Protection Agency, Great Lakes National 
Program, Chicago, Illinois. EPA/905/R-01/013. 

U.S. Environmental Protection Agency. 2001b. 
Ambient Aquatic Life Water Quality for Atrazine. 
U.S. Environmental Protection Agency, Office of 
Water, Washington, D.C. EPA/822/D-01/002, 
230 pp. 


14 



PART 1 


INTRODUCTION 


Chapter 2. General Information on the 
Herbicide Atrazine and Its Degradation 
Products 

Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects 
Research Laboratory 
Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research 
Branch 

Large Lakes Research Station 

9311 Groh Road 

Grosse lie, Michigan 48138 

1.2.1 Background 

Atrazine is a triazine herbicide registered to control 
broadleaf weeds and some grassy weeds by 
inhibiting photosynthesis. Its primary use in the Lake 
Michigan basin is for the control of weeds in corn 
crops. It is estimated to be the most heavily used 
herbicide in the United States. Usage on corn 
accounts for approximately 86% of total United 
States domestic usage, followed by sorghum at 10%, 
and sugarcane at 3% (all other uses make up the 
remaining 1%). For corn crops, it is usually applied 
in the spring prior to, during, or after planting a crop 
or after crop emergence. The product is formulated 
as an emulsifiable concentrate, flowable concentrate, 
water dispersible granular (dry flowable), soluble 
concentrate, wettable powder, granular, and as a 
ready-to-use formulation. It may be applied to the 
field with a groundboom sprayer, aircraft, or by 


means of a tractor-drawn spreader (U.S. 
Environmental Protection Agency, 2003a).. In a 
1990-1991 period, atrazine was the single highest- 
use pesticide in the Great Lakes basin (United States 
only) and represented 19.4% of all pesticides used 
on agricultural crops (U.S. General Accounting 
Office, 1993). 

Atrazine was registered in 1958 (U.S. Environmental 
Protection Agency, 2003a), and is currently 
undergoing a reregistration review. Syngenta is the 
primary atrazine registrant. Pesticides registered 
prior to November 1984 are subject to the 
reregistration process. On January 31, 2003, the 
U.S. Environmental Protection Agency (USEPA) 
issued an Interim Reregistration Eligibility Decision 
(IRED) for atrazine (U.S. Environmental Protection 
Agency, 2003b). In October 2003, the USEPA 
issued an addendum that updates the January 31, 
2003 IRED (U.S. Environmental Protection Agency, 
2003c). The Agency expects the registrants to adopt 
the risk management measures presented in the 
IRED. Among other requirements, the IRED 
mandates the monitoring of 40 representative 
watersheds in the United States to determine if 
specific atrazine levels of concern are exceeded, a 
testing program to better evaluate potential risk to 
amphibians, and measures to mitigate exposure risk 
to applicators in both residential and agricultural 
settings. Watersheds exceeding levels of concern 
criteria will be subject to remedies under the 
USEPA’s Total Maximum Daily Load (TMDL) 
program requirements. In the October 2003 
addendum to the IRED, the Agency concluded that 
there is sufficient evidence to formulate a hypothesis 
that atrazine exposure may impact gonadal 


15 





development in amphibians, but there are currently 
insufficient data to confirm or refute the hypothesis. 
On October 9-12, 2007, the Federal Insecticide, 
Fungicide, and Rodenticide Act (FIFRA) Scientific 
Advisory Panel (SAP) met with the Agency to 
evaluate the potential for atrazine to affect the 
development of amphibian species. However, in an 
October 2007 report to the FIFRA Scientific Advisory 
Panel, the Agency’s review of the literature indicated 
that studies do not show that atrazine produces 
consistent, reproducible effects across the range of 
exposure concentrations and amphibian species 
tested. Based on available test data, atrazine is not 
likely to be a human carcinogen. The Agency does 
have concern in regards to the potential hormonal 
effects observed in laboratory animals exposed to 
atrazine. A Reregistration Eligibility Decision (RED) 
was issued for atrazine, a triazine pesticide, in April 
2006. In that RED, an evaluation was performed to 
determine if the cumulative effect from the triazine 
pesticides (atrazine, simazine, propazine, and their 
chlorinated degradates) that share a common 
mechanism of toxicity are below the Food Quality 
Protection Act (FQPA) regulatory level - that the 
risks associated with the pesticide residues pose a 
reasonable certainty of no harm. 

A comprehensive review of atrazine toxicity to 
various freshwater trophic groups was conducted by 
Solomon et al. (1996). A total of 85 species were 
tested, and the order of sensitivity from most to least 
sensitive trophic groups was as follows: 
phytoplankton > aquatic macrophytes > benthos > 
zooplankton > fish. Due to limited data, amphibians 
were not included in this sensitivity review. Atrazine 
was found to be more inhibitory to photosynthesis 
than were its transformation products. Atrazine was 
seven to 10 times more inhibitory to blue-green algae 
and four to six times more inhibitory to green algae 
than the most potent transformation product, 
deethylatrazine (DEA). Young fish survival may be 
at risk if the atrazine exposure concentrations are 
significant enough to impact phytoplankton 
populations and macrophytes. Zooplankton, an 
important food source for juvenile fish, may be 
depleted if the phytoplankton are reduced, and the 
juvenile fish may become easier prey if they lose the 
protective cover of macrophytes. 


Atrazine is often found in surface water and is 
regulated under the Safe Drinking Water Act. A 
Maximum Contaminant Level (MCL) of 3 ppb was 
established in 1991 by the USEPA’s Office of Water 
(U.S. Environmental Protection Agency, 1995). 
Loadings associated with run-off from farm fields are 
often seasonal with the spring and early summer¬ 
time periods being the highest. For municipalities 
dependent upon drinking water supplies from rivers, 
potential exceedences of the MCL are most likely to 
occur from mid-April through mid-July in the Lake 
Michigan basin when atrazine concentrations are 
most likely to be high. Using a variety of bench-scale 
water treatment processes such as coagulation, 
softening, ozonation, chlorination, and powdered 
activated charcoal, researchers had difficulty 
adequately removing atrazine from the water and 
recommended that other removal processes should 
be investigated (Westerhoff et al., 2005). 

Atrazine has been banned in the European Union 
(EU) since October 4, 2003 when the herbicide was 
not granted re-registration. This decision was taken 
by the Standing Committee on the Food Chain and 
Animal Health (SCFAH), the EU regulatory body. 

Additional background information on atrazine and 
access to the documents cited in this section can be 
downloaded at http://www.epa.gov/pesticides/. 

1.2.2 Physical-Chemical Properties of 
Atrazine 

Physical and chemical properties of atrazine are 
given in Table 1.2.1. With a low Henry’s law 
constant, atrazine volatilization from the lake is low. 
Also, with a moderate solubility in water, run-off from 
farm fields can occur, especially in the spring after 
significant rainfall and when soil moisture content is 
high. With a low octanol-water partition coefficient 
(K ow ), atrazine is not strongly sorbed to particles in 
the water, and it is not bioaccumulated to any extent. 
Frank et al. (1979) analyzed suspended solids from 
12 streams (45 samples) in 1974 and 1976 flowing 
into the Great Lakes from the Canadian side 
(Ontario) and were unable to detect atrazine in these 
particulates (detection limit of 0.05 pg/g). However, 
of the 92 streams sampled in 1977, they detected 
atrazine in the water approximately 80% of the time. 
From that study, they concluded that atrazine was in 


16 




Table 1.2.1. Physical and Chemical Properties of 
Atrazine 


Empirical Formula 
Chemical Name 

Chemical Family 


C 8 H 14 CIN 5 

2-chloro-4-ethylamino-6- 
isopropylamino-1,3,5-triazine 
Triazine 


Other chemical compounds, such as cyanazine and 
simazine, with the same triazine ring structure as 
atrazine have been used in the Great Lakes 
watershed. Cyanazine usage in the basin in the 
early 1990s was about 40% that of atrazine, and 
simazine was approximately 1 % that of atrazine (U.S. 
General Accounting Office, 1993). Both cyanazine 
and simazine were used as herbicides. 


Structural Formula: 


Cl 

I 

C 

// \ 

(CH 3 ) n n 

I I II 

(CH 3 )—(CH)—(NH)—C C—(NH)—(CH 2 )—(CH 3 ) 

\\ / 

N 

215.7 g/mol 
173°C to 175°C 
40 pPa at 20°C 
33 PPM at 25°C 
0.35 g/ml 

8.1 x 10' 8 (dimensionless) at 
25°C (U.S. Department of 
Agriculture, 2001) 

White crystalline solid 
2.7645 


Molecular Weight 
Melting Point 
Vapor Pressure 
Solubility in Water 
Density 
Henry's Law 
Constant 

Physical state 

log K™ 


the dissolved phase, rather than attached to 
particles. Laboratory measurements of the partition 
coefficients for atrazine, DEA, and 
deisopropylatrazine (DIA) resulted in the following: 
1.1, 0.4, and 0.3 (ng/g)/(ng/ml), respectively. The 
particulate substrate was Eudora Silt Loam with a 
1.0% carbon content. These results indicated that 
the two degradation products are even more soluble 
than the parent compound, atrazine (Mills and 
Thurman, 1994). So, models often omit the 
interaction of atrazine with solids (both suspended 
solids and sediment) and do not include 
bioaccumulation components. Because atrazine is 
primarily transported in a dissolved phase, 
groundwater is vulnerable to contamination as it can 
receive a load associated with infiltration. 


Unless otherwise specified, the information in Table 

1.2.1 was obtained from USEPA’s Office of 
Pesticides (January 2003a). 

1.2.3 Atrazine Degradation 

Atrazine is known to degrade in the environment 
through either biotic or abiotic processes. The 
specific bacteria strain and population, physical and 
chemical conditions present, and media type all 
contribute to determining the degradation fate of 
atrazine in the environment. 

1.2.3.1 Biotic Degradation in Surface Water 

Bacterial processes are known to convert atrazine to 
DEA and DIA; however, this degradation is not likely 
occurring in the surface water. Abiotic processes 
often convert atrazine to hydroxyatrazine. See 
Figure 1.2.1 for the chemical structures of these 
major degradation products. Biodegradation assays 
of 14- to 32-days of unfiltered water from the River 
Po, Italy, spiked with various concentrations of 
atrazine, yielded no degradation products (Brambilla 
et al., 1993). Ingerslev and Nyholm (2000) tested 
natural water samples from an unpolluted forest 
stream using 14 C-labeled atrazine. Microbial 
degradation of atrazine was evaluated by measuring 
the evolution of 14 C in carbon dioxide (C0 2 ). Testing 
these samples with a wide range of atrazine 
concentrations typically found in streams showed that 
the natural population of microbes did not degrade 
the labeled atrazine. Biodegradation of atrazine was 
not found in two shallow impounded small lakes in 
Nebraska that receive agricultural inputs of atrazine 
from run-off (Spalding et al., 1994). Half-lives of 
atrazine in these lakes were estimated to range from 
193 to 124 days. The biodegradation product, DEA, 
was not increasing relative to atrazine in the lake, 
therefore suggesting that the degradation observed 
was not biotic. They surmised that degradation was 


17 





/ 


Cl 



Atrazine 


Cl 



NHC 2 H 5 


Deisopropylatrazine 


Cl 



Deethylatrazine 


OH 



Hydroxyatrazine 


Figure 1.2.1. Chemical structures of atrazine and 
its major degradation products. 


due to abiotic processes. Evidence of 
biodegradation was not found in a study of a lake in 
Nebraska (Ma and Spalding, 1997). However, these 
researchers did suggest that abiotic degradation was 
the likely mechanism for degradation. A study of 
atrazine degradation in an Iowa stream determined 
that atrazine biodegradation was not occurring in the 
river (Kolpin and Kalkhoff, 1993). Modeling analysis 
of a small Swiss lake (hydraulic detention time of 1.2 


years) found that atrazine is rather stable in the lake 
water with removal primarily due to export with water 
flowing out of the lake (Buser, 1990; Ulrich et al., 
1994; Muller et al., 1997). Atrazine degradation via 
biotic and/or abiotic processes in Lake Michigan was 
found to be negligible using a mass balance model 
(Rygwelski etal., 1999). Biodegradation products of 
atrazine are commonly found in surface waters, but 
their origin is likely from agricultural soils where 
biodegradation is known to occur to a significant 
extent. 

There are various hypotheses why researchers 
cannot find evidence of atrazine biodegradation in 
surface water. In systems such as Lake Michigan, 
this potential biotic “food” source (atrazine) is very 
dilute, and therefore, it is hypothesized that bacteria 
specific to atrazine degradation do not thrive. If 
atrazine were to substantially partition to particles in 
the water, then perhaps atrazine would be in a more 
concentrated form that could sustain the specific 
strain of atrazine-degrading bacteria. Using granular 
activated charcoal to enhance atrazine adsorption 
and the inclusion of atrazine-specific bacterial 
degraders in a laboratory batch reactor, significant 
reductions (45% to 86%) in atrazine concentrations 
were achieved after a 15-day incubation period at 
10°C (Feakin et al., 1994). Also, if present in 
sufficient quantities, more readily available sources 
of nitrogen other than that provided by the 1,3,5- 
triazine structure may be preferentially used by the 
atrazine-degrading bacteria. Therefore, the atrazine 
triazine structure would be left intact (Feakin et al., 
1994). Typically, the first stage in the biodegradation 
of the 1,3,5-triazines is deisopropylation and 
deethylation leading to the removal of nitrogen from 
positions four and six of the 1,3,5-triazine ring. 
Feakin et al. (1994) also showed that degradation in 
water without sufficient assimilable organic carbon 
did not support biodegradation. They theorized that 
the bacteria needed a certain minimum level of 
carbon for maintenance energy and growth. 

While atrazine biodegradation is not likely to occur 
naturally in surface waters, efforts have been made 
to find ways to create better conditions for biotic 
degradation in water in laboratory operations, with 
the intent of applying the methodology to water 
treatment facilities. A pilot plant operation studying 
the potential to degrade atrazine in water found that 


18 











an atrazine-specific degrading bacterium, 
Rhodococcus rhodochrous strain SL1, was effective 
in degrading the herbicide after the atrazine was 
adsorbed to granular activated carbon packed in 
columns (Jones et al., 1998). However, periodic 
reinoculation onto the columns was required to 
maintain adequate numbers of SL1. Conventional 
water treatment facilities are not effective in reducing 
atrazine concentrations. Conventional activated 
sludge wastewater treatment plants are also 
ineffective at removing atrazine from the waste 
stream (Monteith et al., 1995). 

1.2.3.2 Abiotic Degradation in Surface Water 

\ 

1.2.3.2.1 Hydrolysis 

Degradation by hydrolysis is likely in water if the 
environmental conditions are favorable. Hydrolysis 
was not found to occur at pH greater than 4 at 15°C 
in buffered distilled water or natural river water 
(Comber, 1999). Furthermore, the addition of iron 
hydroxide and aluminum silicate did not promote 
degradation via catalysis as some researchers have 
hypothesized. The pH of Lake Michigan is relatively 
high (8.2) and, therefore unlikely to support 
hydrolysis. However, at temperatures of 35°C, 
atrazine was found to slowly degrade via hydrolysis 
at a range of pHs from 3 to 8 in distilled water (Lei et 
al., 2001). Hydrolysis rate constants were increased 
(half-lives shortened) with the addition of humic acids 
and nitrate ions. An evaluation of atrazine hydrolysis 
in groundwater samples at a pH of 7.8 and 
temperatures of 4°C and 30°C showed no significant 
loss (Widmer et al., 1993). Also, when hydrolysis 
experiments were conducted at room temperature 
and a pH of 6.5, dissolved organic carbon (DOC) 
additions with and without nitrate did not cause any 
degradation (Hapeman et al., 1998). Spalding et al. 
(1994) theorized that surface catalyzed hydrolysis 
was a possible mechanism for degrading atrazine in 
two small lakes located in Iowa. These shallow lakes 
had high turbidity with high DOC (5.1 to 8.4 mg/I). 

1.2.3.2.2 Photolysis 

Photolysis is enhanced when nitrate ions are present 
to facilitate indirect photolysis by acting as a catalyst. 
It is hypothesized that in the presence of the nitrate 
ion, hydroxy radicals are produced resulting in 


oxidation and/or removal of the alkyl groups. In a 
small stream in Iowa, isolated from groundwater 
intrusion, Kolpin and Kalkhoff (1993) found that the 
atrazine half-life had a significant inverse relationship 
with sunlight, suggesting that photolysis was 
responsible. This same inverse relationship was 
noted in a reservoir in Iowa (Chung and Gu, 2003). 
However, in both of these studies a correlation 
between atrazine half-lives and concentrations of 
nitrate ions was poor. The relationship between half- 
lives and nitrate concentrations may be masked in 
the natural environment because of the strong 
seasonality of photodegradation with sunlight. Using 
titanium dioxide (Ti0 2 ) as a photocatalyst and 
simulated solar light in a laboratory setting, 
researchers have found that atrazine can be 
degraded very rapidly (Pelizzetti et al., 1990) with a 
half-life estimated at 19 minutes (Konstantinou et al., 
2001a). Some DOC mimics can significantly 
increase photodegradation of atrazine, while others 
do not, leading researchers to believe that both the 
structural properties and concentration of DOC in 
water are important factors to consider when 
assessing potential photodegradation impact 
(Hapeman et al., 1998). Using natural light sources, 
some studies have found that structural properties of 
some types of natural DOC present in surface water 
will actually reduce photodegradation rates 
(Konstantinou et al., 2001b). The degradation 
products found in the Konstantinou study using 
natural water samples were the hydroxy and 
dealkylated derivatives of atrazine. It appears that 
light energy at wavelengths less than 300 nm is 
necessary to initiate direct photolysis where 
photolysis occurs without the need of an intermediary 
(Comber, 1999). However, natural sunlight provides 
very little of this light energy. Direct 

photodegradation produces primarily hydroxyatrazine 
(Konstantinou et al., 2001b). 

Even though Lake Michigan has very low nitrate 
(1994-1995 median 0.28 mg/L) and DOC 
concentrations (1994-1995 median 1.5 mg/L), it is 
possible that some degradation is occurring via 
various photolysis processes. However, it is believed 
that the impact on the lake is small because the 
depth of the lake limits light penetration through the 
watercolumn and isolates the hypolimnion during the 
high solar radiation period. Studies of atrazine 
transport, atmospheric deposition, and fate in Isle 


19 



Royale National Park have shown that the shallow 
lakes have lower atrazine concentrations than the 
deeper lakes on this island in Lake Superior 
(Thurman and Cromwell, 2000). These island lakes 
are in a pristine area and receive their atrazine input 
from the atmosphere. If atrazine were highly 
persistent in water, then one would expect that the 
shallow lakes would have higher concentrations than 
the deeper lakes because the shallow lakes have a 
higher surface area to depth ratio. However, 
Thurman and Cromwell’s findings are just the 
opposite, and a possible explanation for this is that 
photolysis in the shallow lakes occurs throughout the 
water column, but in the deeper lakes it may be 
limited to the upper water column only. 

1.2.3.3 Atrazine Degradation in Soil 

The degradation of atrazine in soils is much faster 
than in water. Durand and Barcelo (1992) presented 
half-life values for atrazine in soil from six studies. All 
of the studies found half-lives of 125 days or less. 
Nair and Schnoor (1994) found that degradation 
rates in soil depend strongly on soil environmental 
conditions. Degradation increased with increasing 
soil water and organic carbon content; however, 
degradation rates decreased in low oxygenated soils. 
Mirgain et al. (1993) found that bacteria degrade 
atrazine in soils where the organic carbon content is 
greater than 2%., Degradation increased with 
increasing carbon content. They also noted that 
repeated applications of atrazine on the same soil 
sample results in the enhancement of degradation 
with each successive application. They found that 
the reason for this is that bacteria populations 
specific to degrading atrazine increased with each 
application and the number of bacteria strains 
decreased. Compared to water, soil is better in 
facilitating degradation of atrazine because the “food” 
source (atrazine) is readily available to support 
bacterial strains that are efficient in degrading the 
herbicide. 

References 

Brambrilla, A., B. Rindone, S. Polesselo, S. Galassi, 
and R. Balestrini. 1993. The Fate of Triazine 
Pesticides in River Po Water. Sci. Total Environ., 
132(2/3):339-348. 


Buser, H.-R. 1990. Atrazine and Other s-Triazine 
Herbicides in Lakes and in Rain in Switzerland. 
Environ. Sci. Technol., 24(7):1049-1058. 

Chung, S. and R.R. Gu. 2003. Estimating Time- 
Variable Transformation Rate of Atrazine in a 
Reservoir. Adv. Environ. Res., 7(4):933-947. 

Comber, D.W. 1999. Abiotic Persistence of Atrazine 
and Simazine in Water. Pest. Sci., 55(7):696- 
702. 

Durand, G. and D. Barcelo. 1992. Environmental 
Degradation of Atrazine, Linuron, and 
Fenitrothion in Soil Samples. Toxicol. Environ. 
Chem., 36(3/4):225-234. 

Feakin, S.J., E. Blackburn, and R.G. Burns. 1994. 
Biodegradation of s-Triazine Herbicides at Low 
Concentrations in Surface Waters. Water Res., 
28(11 ):2289-2296. 

Frank, R., G.J. Sirons, R.L. Thomas, and K. 
McMillan. 1979. Triazine Residues in 
Suspended Solids (1974-1976) and Water (1977) 
From the Mouths of Canadian Streams Flowing 
Into the Great Lakes. J. Great Lakes Res., 
5(2): 131-138. 

Hapeman, C.J., S. Bilboulian, B.G. Anderson, and A. 
Torrents. 1998. Structural Influences of Low- 
Molecular-Weight Dissolved Organic Carbon 
Mimics on the Photolytic Fate of Atrazine. 
Environ. Toxicol. Chem., 17(6):975-981. 

Ingerslev, F. and N. Nyholm. 2000. Shake-Flask 
Test for Determination of Biodegradation Rates of 
14 C-Labeled Chemicals at Low Concentrations of 
Surface Water Systems; Ecotoxicol. Environ. 
Safety, 45(3):274-283. 

Jones, L.R., S.A. Owen, P. Horrell, and R.G. Burns. 

1998. Bacterial Inoculation of Granular Activated 
Carbon Filters for the Removal of Atrazine From 
Surface Water. Water Res., 32(8):2542-2549. 

Kolpin, D.W. and S.J. Kalkhoff. 1993. Atrazine 
Degradation in a Small Stream in Iowa. Environ. 
Sci. Technol., 27(1 ):134-139. 


20 



Konstantinou, I.K., T.M. Sakellarides, V.A. Sakkas, 
and T.A. Albanis. 2001a. Photocatalytic 
Degradation of Selected s-Triazine Herbicides 
and Organophosphorus Insecticides Over 
Aqueous Ti0 2 Suspensions. Environ. Sci. 
Technol., 35(2):398-405. 

Konstantinou, I.K., A.K. Zarkadis, and T.A. Albanis. 
2001 b. Photodegradation of Selected Herbicides 
in Various Natural Waters and Soils Under 
Environmental Conditions. J. Environ. Quality, 
30(1): 121 -130. 

Lei, Z., C. Ye, and X. Wang. 2001. Hydrolysis 
Kinetics of Atrazine and Influence Factors. J. 
Environ. Sci., 13(1 ):99-103. 

Ma, L. and R.F. Spalding. 1997. Herbicide 
Persistence and Mobility in Recharge Lake 
Watershed in York, Nebraska. J. Environ. Qual., 
26(1 ):115-125. 

Mills, M.S. and E.M. Thurman. 1994. Reduction of 
Nonpoint Source Contamination of Surface Water 
and Groundwater by Starch Encapsulation of 
Herbicides. Environ. Sci. Technol., 28(1):73-79. 

Mirgain, I., G.A. Green, and H. Monteil. 1993. 
Degradation of Atrazine in Laboratory 
Microcosms: Isolation and Identification of the 
Biodegrading Bacteria. Environ. Toxicol. Chem., 
12(9):1627-1634. 

Monteith, H.D., W.J. Parker, J.P. Bell, and H. 
Melcher. 1995. Modeling the Fate of Pesticides 
in Municipal Wastewater Treatment. Water 
Environ. Res., 67(6):964-970. 

Muller, S.R., M. Berg, M.M. Ulrich, and R.P. 
Schwarzenbach. 1997. Atrazine and Its Primary 
Metabolites in Swiss Lakes: Input Characteristics 
and Long-Term Behavior in the Water Column. 
Environ. Sci. Technol., 31 (7):2104-2113.. 

Nair, D.R. and J.L. Schnoor. 1994. Effect of Soil 
Conditions on Model Parameters and Atrazine 
Mineralization Rates. Water Res., 28(5):1199- 
1205. 


Pelizzetti, E., V. Maurino, C. Minero, V. Carlin, E. 
Pramauro, and O. Zerbinati. 1990. 
Photocatalytic Degradation of Atrazine and Other 
s-Triazine Herbicides. Environ. Sci. Technol., 
24(10):1559-1565. 

Rygwelski, K.R., W.L. Richardson, and D.D. Endicott. 
1999. A Screening-Level Model Evaluation of 
Atrazine in the Lake Michigan Basin. J. Great 
Lakes Res., 25(1 ):94-106. " 

Solomon, K.R., D.B. Baker, R.P. Richards, K.R. 
Dixon, S.J. Klaine, T.W. LaPoint, R.J. Kendall, 
C.P. Weisskopf, J.M. Giddings, J.P. Giesy, L.W. 
Hall, Jr., and W.M. Williams. 1996. Ecological 
Risk Assessment of Atrazine in North American 
Surface Waters. Environ. Toxicol. Chem., 
15(1 ):31 -76. 

Spalding, R.F., D.D. Snow, D.A. Cassada, and M.E. 
Burbach. 1994. Study of Pesticide Occurrence 
in Two Closely Spaced Lakes in Northeastern 
Nebraska. J. Environ. Qual., 23(3):571-578. 

Thurman, E.M. and A.E. Cromwell. 2000. 
Atmospheric Transport, Deposition, and Fate of 
Triazine Herbicides and Their Metabolites in 
Pristine Areas at Isle Royale National Park. 
Environ. Sci. Technol., 34(15):3079-3085. 

Ulrich, M.M., S.R. Muller, H.P. Singer, D.M. 
Imboden, and R.P. Schwarzenbach. 1994. Input 
and Dynamic Behavior of the Organic Pollutants 
Tetrachloroethylene, Atrazine, and NTA in a 
Lake: A Study Combining Mathematical 

Modeling and Field Measurements. Environ. Sci. 
Technol., 28(9):1674-1685. 

U.S. Department of Agriculture. 2001. Agriculture 
Research Service Pesticide Properties Database. 
Available from U.S. Department of Agriculture at 
http://www.ars.usda.gov. 

U.S. Environmental Protection Agency. 1995. 
National Primary Drinking Water Regulations, 
Contaminant Specific Fact Sheets, Synthetic 
Organic Chemicals, Consumer Version. 
EPA/811/F-95/003-T. 


21 






U.S. Environmental Protection Agency. 2003a. 

Pesticides: Topical and Chemical Fact Sheets - 
Atrazine Background. U.S. Environmental 
Protection Agency, Office of Pesticides Program, 
Washington, D.C.. Available from U.S. 
Environmental Protection Agency at http://www. 
epa.gov/pesticides/factsheets/atrazine_ 
background.html. 

U.S. Environmental Protection Agency. 2003b. 

Interim Reregistration Eligibility Decision (IRED) 
for Atrazine. U.S. Environmental Protection 

Agency, Office of Pesticides Program, 
Washington, D.C. Case Number 0062, 285 pp. 

U.S. Environmental Protection Agency. 2003c. 

October 31,2003 Addendum to the January 31, 

2003 IRED. U.S. Environmental Protection 

Agency, Office of Pesticides Program, 
Washington, D.C. 16 pp. 


U.S. General Accounting Office. 1993. Report to the 
Chairman, Subcommittee on Oversight of 
Government Management, Committee on 
Governmental Affairs, U.S. Senate: Pesticides - 
Issues Concerning Pesticides Used in the Great 
Lakes Watershed. U.S. General Accounting 
Office, Washington, D.C. GAO/RCED-93-128, 
39 pp. 

Westerhoff, P., Y. Yoon, S. Snyder, and E. Wert. 
2005. Fate of Endocrine-Disruption, 
Pharmaceutical, and Personal Care Product 
Chemicals During Simulated Drinking Water 
Treatment Processes. Environ. Sci. Technol., 
39(17):6649-6663. 

Widmer, S.K., J.M. Olson, and W.C. Koskinen. 
1993. Kinetics of Atrazine Hydrolysis in Water. 
J. Environ. Sci. Health, 28(1 ):19-28. 


22 



PARTI 


INTRODUCTION 


Chapter 3. Atrazine Field Data 
Observations 

Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects 
Research Laboratory 
Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research 
Branch 

Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 
and 

Harry B. McCarty, Ken Miller, Robert N. Brent, and 
Judy Schofield 
DynCorp (a CSC Company) 

601 Stevenson Avenue 
Alexandria, Virginia 22304 

1.3.1 Background 

In this chapter, a summary of the Lake Michigan 
Mass Balance Project (LMMBP) atrazine data and 
historical data are presented along with a brief 
description of sampling and analytical methodology. 
A LMMBP atrazine data report by DynCorp Science 
and Engineering Group was prepared that provides 
more details regarding concentrations of atrazine and 
its degradation products related to sampling 
atmospheric components, tributaries, and the open- 
lake water column (Brent et al., 2001). The DynCorp 
data report also provides an in-depth discussion on 
data quality implementation and assessment. Also, 
see Part 1, Chapter 1, Section 1.1.6 in this report for 


references to additional documents, such as the 
LLMBP Methods Compendium and quality assurance 
plans, that provide additional details on the project. 
Project data reside in a Great Lakes National 
Program Office (GLNPO)-managed Great Lakes 
Environmental Monitoring Database (GLENDA). The 
data were collected for use in the mass balance 
models. 

For the LMMBP, measurements of atrazine, along 
with two degradation products deisopropylatrazine 
(DIA) and deethylatrazine (DEA), were attempted for 
all media. However, for some media, the detection of 
the degradation products was difficult because 
atrazine concentrations were very low. Whenever 
possible, Principal Investigators (Pis) were requested 
to report analytical results as measured, even if the 
value was lower than the method detection limit. 
This modeling report focuses on modeling atrazine 
and not the degradation products because of the 
sparsity of degradation data for some media. Also, 
triazines other than atrazine can degrade into DEA 
and DIA (Thurman et al., 1994). So, if all of the 
parent compounds are not modeled, the degradation 
products cannot be modeled. In a summary report 
(U.S. General Accounting Office [USGAO], 1993) for 
pesticide usage in the basin for 1990 and 1991, two 
other triazines used as herbicides in the Lake 
Michigan basin (simazine and cyanazine) can 
degrade into DIA. Simazine usage in the basin was 
extremely low compared to atrazine usage so 
degradation products from simazine would be 
insignificant. Cyanazine usage, however, was about 
37% of the atrazine usage estimates. Propazine can 
degrade into DEA, but this chemical was not listed in 
the usage tables of the USGAO report. It has 


23 



been estimated that atrazine is the major source for 
DEA (98%) in the Corn Belt (Thurman et a!., 1994). 

Hydroxylated atrazine degradation products are also 
found in the environment (Lerch et at., 1998) and are 
formed by substitution of the chlorine atom with a 
hydroxyl group in the parent atrazine compound. In 
a survey of Midwestern streams, they found that 
these hydroxylated atrazine degradation products 
were less abundant than DEA and DIA. 
Hydroxyatrazine is the primary hydroxylated atrazine 
degradation product but was not measured in the 
LMMBP. 

1.3.2 Atmospheric Components 

Atrazine enters the atmosphere by volatilization from 
either agricultural land (soil and plant) or water, by 
wind erosion from fields where the chemical is either 
sorbed onto soil particles or as a pure pesticide 
particle from plant or soil surfaces, and by physical 
drift of spray during application (Banks and Tierney, 
1993). Once the chemical is airborne, a variety of 
physical and/or chemical processes can cause 
degradation, and various physical processes can 
cause deposition back to land or water. In addition to 
the atrazine data report by DynCorp and the LMMBP 
Methods Compendium, information on atmospheric 
media sampled can be found in a master’s thesis by 
Sondra Miller (Miller, 1999). 

1.3.2.1 Sampling and Analytical Methodology 

Primary atmospheric sampling occurred at eight 
shoreline stations. Sampling locations are identified 
in Figure 1.1.2. Some limited atmospheric sampling 
also occurred at selected open-lake stations aboard 
the research vessel, Lake Guardian. Also, three 
stations (Eagle Harbor, Michigan; Brule River, 
Wisconsin on the southern shore of Lake Superior; 
and Bondville, Illinois) were located outside of the 
basin and were established to characterize air 
masses from the southwest or northwest directions. 
Vapor, particulate, and wet deposition were sampled 
and analyzed. Atmospheric sampling occurred from 
March 15, 1994 to October 20, 1995. A total of 294 
vapor phase samples, 226 particulate samples, and 
207 precipitation samples were collected. All 
samples were analyzed for atrazine and primary 
degradation products DEA and DIA except for the 


Sleeping Bear Dunes site where only atrazine was 
analyzed. From April 1994 through July 1994, 
samples from the Sleeping Bear Dunes site were 
collected and analyzed at the Illinois State Water 
Survey (Clyde Sweet). For the remaining period at 
the same site, August 1994 through October 1995, 
atmospheric samples were both collected and 
analyzed by Indiana University (Ronald Hites). 
Samples from all other atmospheric stations were 
collected and analyzed by the Illinois State Water 
Survey. 

Wet deposition composite samples were collected 
over a 28-day period at the shore-based stations 
using a Meteorological Instruments of Canada (MIC- 
B) sampler modified with a heater for all-weather 
sampling. Equipped with a precipitation sensor, the 
sampler was open to the atmosphere only during wet 
events. Rain and snow that was collected flowed 
through a 30 cm XAD-2 resin column that absorbed 
the atrazine and degradation products from the 
sample. Glass wool plugs, before and after the 
column, prevented particles from entering the 
column. After the required collection period, the 
collection funnel was rinsed with water and wiped 
with clean quartz fiber filter paper to remove any 
adhering particles. Both the filter paper and the 
rinsing became part of the sample. Five percent of 
Illinois State Water Survey wet deposition samples 
were field duplicates with a system precision of 115% 
for samples above the method detection limit. The 
mean laboratory matrix spike recovery was reported 
at 82%. Indiana University analyzed only 14 routine 
samples and 12 field duplicates. Their system 
precision of the duplicates was 28.1% for samples 
above the method detection limit. They achieved 
laboratory matrix spike recovery of 110%. 

Composite atmospheric vapor and particulate 
samples were collected over a period of 24 hours 
every 12 days using a high-volume air sampler. Air 
was passed through a XAD-2 resin to collect the 
atrazine and degradation products. Air flow was 
maintained at approximately 34 m 3 /hour during 
sampling. Resin traps were wrapped in aluminum foil 
and sealed in tin cans and held at -18°C until 
analysis. Particulate phase atmospheric samples 
were collected on pre-fired quartz fiber filters. Filters 
were wrapped in aluminum foil and sealed in tin cans 
and stored at -18°C until analysis. Multiple 24-hour 


24 



samples were often composited to yield a monthly 
sample composite. At the Sleeping Bear Dunes site, 
and occasionally some other sites, 24-hour samples 
were analyzed individually and then mathematically 
composited to yield a monthly average. 

The XAD-2 resin or filter samples were extracted by 
Soxhlet extraction with 300 ml of a 1:1 hexane and 
acetone mixture. The extract was concentrated by 
rotary evaporation and then cleaned-up with 3% 
deactivated silica with a sodium sulfate cap to 
remove non-target interfering compounds. Samples 
were analyzed using gas chromatography coupled to 
a mass spectrometer detector. 

1.3.2.2 Results 

1.3.2.2.1 Atrazine in the Gas Phase Fraction 

Gas phase samples were extremely low; therefore, 
quantifying over-the-lake concentrations used in the 
volatilization and absorption mass balance algorithms 
was difficult. Only 11 samples were above the 
detection limits. And of these samples, four were 
flagged by the analysts as possibly contaminated due 
to field or laboratory blanks, and four others were 
from a station outside the Lake Michigan basin 
located in Bondville, Illinois. This leaves three 
samples collected in the basin with measurements of 
atrazine above the detection limit-one sample from 
South Haven collected July 7, 1994 through July 8, 
1994 at 70 pg/m 3 and the rest at Sleeping Bear 
Dunes collected November 16,1994 - November 17, 
1994 at 22.1 pg/m 3 and September 9, 1995- 
September 14, 1995 at 31.5 pg/m 3 . The sampling 
stations at both the Bondville and South Haven sites 
are located where local agricultural influences on the 
gas phase concentrations are likely. Therefore, 
these concentrations may not be representative of 
gas phase concentrations over-the-lake. Peck and 
Hornbuckle (2005) measured gas phase 
concentrations of atrazine in the intensively farmed 
state of Iowa. They found that gas phase atrazine 
concentrations showed a seasonal pattern with 
highest concentrations evident during the spring and 
early summer. In their study, the average 
concentration of atrazine in the air was 1,200 pg/m 3 . 
In the LMMBP study, DEA and DIA were not 
detected in the gas phase. 


Because gas phase measurements did not provide a 
reliable over-the-lake estimate in the LMMBP, we 
made assumptions about this value based on 
detection limits. In Miller (1999), the method 
detection limit (MDL) for atrazine for the shore-based 
and open-water sites gas phase concentration was 
21.3 ng. Knowing the average flow rate of air 
through the sampler and assuming a 24-hour 
collection period, Miller estimated a MDL of 9.26 
pg/m 3 . Modeling scenarios presented in this report 
utilized this method detection limit to place an upper 
expected limit on this boundary condition. 

1.3.2.2.2 Atrazine in the Particulate Fraction 

Atrazine in the particulate fraction in air was low and 
often difficult to detect. This finding is also supported 
by other studies, such as in rural Iowa - a state with 
the highest pesticide applications in the United States 
and where 94% of the state is farmland and 60% of 
that area is planted with corn (Nations and Hallberg, 
1992). Only 23% of the particulate samples taken for 
the LMMBP had atrazine concentrations above the 
sample-specific detection limit. Also, the chemical 
was primarily observed in the months of April, May, 
June, and July. Only one particulate sample 
collected from August through March contained 
levels above the MDL. Maximum monthly average 
atrazine concentrations ranged from a low of 160 
pg/m 3 at Sleeping Bear Dunes in northern Lake 
Michigan to a high of 1,400 pg/m 3 at the Bondville 
site. The elevated concentration at the Bondville site 
is most likely related to the fact that it is in the middle 
of an intensive corn-growing region. A summary of 
spring/summer atrazine concentrations measured in 
the particulate phase can be found in the atrazine 
data report (Brent et al., 2001). Particle size 
distribution analyses were not conducted on 
particulates collected in the air samples. Sweet and 
Harlin (1998) estimated that approximately 1% of the 
total atrazine load associated with wet and dry 
particle deposition to Lake Michigan is due to 
atrazine associated with particulates. 

Of the over-water sampling stations, only two 
samples had detectable atrazine in the particulate 
fraction, and both of these samples were collected 
close to land in the southern part of the lake (near 
Chicago and Indiana Dunes). An atrazine 
concentration of 560 pg/m 3 was measured at station 


25 





1 in May 1994, and a concentration of 280 pg/m 3 was 
measured at station 5. Station 1 is shown on Figure 
1.1.2 as the southern-most over-water atmospheric 
monitoring station, and station 5 is located 
immediately north and slightly west of station 1. 

In the spring/summer of 1994, the LMMBP project 
detected atrazine but not the degradation products at 
the Eagle Harbor site, which is located in Michigan’s 
Upper Peninsula near Lake Superior. From a period 
of early April to mid-September 1995, atrazine 
sampled at Eagle Harbor was detected 
approximately 34% of the time in the particulate 
fraction but not in the vapor phase at this remote site 
(Foreman et al., 2000). In addition, both DIA and 
DEA were detected in the particulate phase. This 
suggests that long range transport is possible for 
both atrazine and the two degradation products via 
particles. 

Having a higher detection limit than the Foreman et 
al. (2000) study may be one reason why atrazine was 
difficult to detect over Lake Michigan in the LMMBP 
study. Foreman’s detection limit was 6 pg/m 3 . For 
the LMMBP, the detection limit ranged from 3.0 to 68 
pg/m 3 (average of 17 pg/m 3 ) for particulate phase 
samples analyzed at the Illinois Water Survey, and 
from 26.8 to 284 pg/m 3 (average of 70.7 pg/m 3 ) for 
samples analyzed at Indiana University. Another 
possible reason for the lack of particulate atrazine 
data over-the-lake is that the type of particulate 
matter carrying atrazine may not be transported very 
far from the source. As a consequence of the low 
number of detects at land-based collection sites, and 
the lack of evidence of atrazine-associated 
particulate fluxes over-the-lake, these fluxes were not 
estimated for modeling purposes. 

1.3.2.2.3 Atrazine and Degradation Products in 
Wet Deposition 

Atrazine in wet deposition was primarily detected in 
the spring and summer months. This seasonality 
was also reported by Nations and Hallberg (1992) 
and Goolsby et al. (1993). All LMMBP samples 
collected in April and May had detectable levels of 
atrazine. Atrazine was not detected in samples from 
November through February. DEA and DIA were 
also primarily detected in the spring and summer 
months. DEA was detected in samples collected 


from March through August, and DIA was only 
detected in samples collected from April through 
June. DEA had a higher frequency of being detected 
and also had a higher concentration on average than 
DIA. Twenty-eight day maximum atrazine 
concentrations measured over 1994 and 1995 
ranged from 100 ng/L at Eagle Harbor to 2,800 ng/L 
at the Indiana Dunes site. The high Indiana Dunes 
value was associated with a low volume sample 
collected over a 28-day sampling period and may 
have been influenced by emissions from nearby 
agricultural fields. During a rain event, atrazine 
concentrations are often much higher at the 
beginning of the event compared to concentrations 
measured at the end of the event (Nations and 
Hallberg, 1992; Goolsby et al., 1993). Nations and 
Hallberg (1992) also found that a rain event closely 
following an earlier rain event by a day or two had 
much lower concentrations (and often non- 
detectable) levels of atrazine in the wet deposition 
sample. Presumably the first event scavenges the 
available pesticide in the atmosphere. Without a 
detailed record of the number and duration of rain 
events in the Indiana Dunes sample, it is difficult to 
conclude if any of the scavenging circumstances 
occurring early in a rain event(s) comprised a major 
volumetric proportion of the sample collected. 
Nations and Hallberg (1992) also found that atrazine 
concentrations in wet deposition tend to be higher in 
regions that have higher usage of atrazine. They 
found consistent, striking differences between two 
stations only 11 km apart. One station located 
adjacent to a row-cropped field had a much higher 
reported value compared to a station located in a 
forested region. Volume-weighted mean LMMBP 
spring/summer atrazine levels for the two-year 
sampling period (1994-1995) ranged from 19 ng/L at 
Eagle Harbor to 120 ng/L at Indiana Dunes. Due to 
the high variability of wet deposition concentrations 
of atrazine at sites, stations around the lake were not 
statistically different based on the Kruskal-Wallis test. 
Sampling at over-water stations was limited. A 
southern central lake station contained 7.5 ng/L on 
August 20, 1994 and a station in Green Bay 
contained 29 ng/L on April 12, 1995. 

Concentrations of atrazine and DEA in wet deposition 
in 1995 were much lower than observations in 1990, 
1991, and 1994. The concentrations of atrazine 
collected in the Lake Michigan basin, as reported in 


26 




Table 1.3.1 for 1990 and 1991, compare very well to 
other data collected by Goolsby et al. (1997) across 
the Midwestern and Northeastern United States. 
They found a range of 200 to 400 ng/L for 1990- 
1991. In 1994, atrazine was found in LMMBP rain 
samples collected between mid-March and mid-April, 
even though corn planting had not yet begun in 
southern Wisconsin. This suggests that atrazine was 
being transported long range, originating from farm 
fields in more southerly states that had been planted 
earlier in the season. In 1995, however, the 
occurrence of atrazine in wet deposition more closely 
coincided with application in the region (Sweet and 
Harlin, 1998). Further evidence that long range 
transport of atrazine to Lake Michigan was minimal in 
1995 is reflected in a low deethylatrazine/atrazine 
ratio (DAR) (0.145) for 1995 (Table 1.3.1). The DAR 
was calculated using the volume-weighted means for 
DEA and atrazine. Generally, higher DAR ratios 
represent higher levels of degradation of atrazine to 
DEA. Long range transport allows more time for 


degradation of atrazine to occur in the air mass. 
DAR ratios were calculated for Isle Royale, a 
wilderness national park in Lake Superior, and the 
ratio at the park was calculated to be approximately 
0.4 (Thurman and Cromwell, 2000) for the study 
period 1991 -1994. So in regards to DAR and except 
for 1995, the two areas (Isle Royale and Lake 
Michigan) appear to compare very well, suggesting 
that under normal circumstances, transport of 
atrazine from distant sources does occur in the wet 
deposition phase. 

A possible explanation of the low atrazine 
concentrations for 1995 is that the spring of 1995 
was cold and wet in major corn-growing areas south 
and west of the Lake Michigan basin compared to 
1991 and 1994. This may have limited long range 
transport to the Lake Michigan basin. Omaha, 
Nebraska and Peoria, Illinois were selected as being 
representative of that area south and west because 
they are located in geographic areas where the 


Table 1.3.1. Summary of Wet Deposition Annual Volume-Weighted Mean Deethylatrazine (DEA) 
Concentrations, Atrazine Concentrations, and Deethylatrazine/Atrazine Ratios (DAR) for All Stations 
in the Lake Michigan Basin 


Year 

Deethylatrazine 

ng/L 

Atrazine 

ng/L 

Deethylatrazine/Atrazine 
Ratios (DAR) 

Sampling Data Range 

1990' 

101.0 

259.0 

0.402 

3/27/1990 - 8/14/1990 

1991' 

233.0 

432.0 

0.540 

4/2/1991 -7/9/1991 

1994 2 

32.4 

80.6 

0.422 

3/15/1994 - 7/5/1994 

1995 2 ‘ 

4.02 

30.0 

0.145 

3/14/1995- 8/31/1995 

Mean 

92.6 

200 

0.377 


Mean (Year 

122 

257 

0.455 


1995 Excluded) 






'Data from Goolsby et al., 1995. All data were used in calculating the volume-weighted mean concentrations. 
Data reported with the detection limit were converted to half the detection limit. DAR represents only 
situations where both the reported DEA and atrazine concentrations were above the detection limit. 

2 Data from the LMMBP. All data, including zeros, were used in calculating the volume-weighted mean 
concentrations. DAR represents only situations where both reported DEA and atrazine concentrations 
were above the detection limit. 


27 






greatest spring atrazine emissions were estimated 
(Scholtz et al., 1997). Figure 1.3.1 shows the 
monthly precipitation at these cities for the important 
months when wet deposition fluxes are normally high 
(National Climatic Data Center, 2000). For both 
Peoria and Omaha, 1991 and 1994 were similar in 
rainfall events; however, for 1995, the months of April 
and May were wetter than the other two years. 
Figure 1.3.2 shows the monthly average 
temperatures for the same two cities. For both 
Peoria and Omaha, 1991 and 1994 were similar in 
average temperatures; however, for 1995 the months 
of April and May were colder than the two other 
years. Not only were the LMMBP atrazine 
concentrations in precipitation low for 1995, but the 
total atrazine deposition for 1995 was approximately 
half of what it was in 1994. This cannot be explained 
by very low precipitation in the Lake Michigan basin 
for 1995. Table 1.3.2 displays the combined mean 
precipitation amounts from Chicago, Illinois; South 
Bend, Indiana; Muskegon, Michigan; Grand Rapids, 
Michigan; Green Bay, Wisconsin; and Milwaukee, 
Wisconsin for 1994 and 1995 (34.26 and 33.73 
inches, respectively) and they are close to the 30- 


year mean of all these sites (34.22 inches). 
Comparisons to a 50-year mean for over-lake 
precipitation to Lake Michigan can be found in 
Figures 1.4.11 and 1.4.12 in Part 1, Chapter 4 of this 
report and show similar results. Also, the 
differences between 1994 and 1995 cannot be 
explained by differences in amounts of atrazine 
applied in the basin between the two years, because 
these amounts are nearly the same (see the atrazine 
loading chapter for more information). The 
differences may be explained by the cold and wet 
spring in the south and west corn-growing regions 
relative to the Lake Michigan basin. In a cold and 
wet spring, less atrazine emission would be expected 
to occur because temperature is a driving force of 
atrazine volatilization from the soil to the air. In the 
wet spring of 1995, among both the Peoria and 
Omaha stations, there was one rain event in April 
over one inch and seven events in May where rainfall 
was over one inch (and as high as 2.5 inches on May 
8 at one of the stations). For spring 1994, there was 
only one rainfall event among the two stations 


12 

10 


in 

<1) 

o -8 
cz 



03 

Q. 

'o 


<D 4 
Q_ 


2 


0 

April April April May May May June June June 

1991 1994 1995 1991 1994 1995 1991 1994 1995 


| Peoria, IL 
m Omaha, NE 



Month and Year 


Figure 1.3.1. Monthly precipitation amounts at cities in two large corn-growing regions. Data are from 
Peoria, Illinois and Omaha, Nebraska. 


28 

































































] Peoria. IL 



April April April May May May June June June 

1991 1994 1995 1991 1994 1995 1991 1994 1995 


Month and Year 

Figure 1.3.2. Monthly average temperatures at cities in two large corn-growing regions. Data are from 
Peoria, Illinois and Omaha, Nebraska. 


Table 1.3.2. Annual Mean Precipitation Amounts Measured at Chicago, Illinois; Fort Wayne, Indiana; 
South Bend, Indiana; Muskegon, Michigan; Grand Rapids, Michigan; and Milwaukee, Wisconsin 


Time Period 

Total Inches (Mean at All Sites) 

Standard Deviation 

30 Years 

34.22 

3.57 

1994 

34.26 

6.94 

1995 

33.73 

4,61 


over one inch and that was on June 22, 1994. 
Perhaps the frequent rainfall events in 1995 washed 
significant quantities of atrazine from the fields into 
streams, rivers, reservoirs, and groundwater via 
infiltration which allowed less of the atrazine to 
volatilize from the farm fields. Atrazine that is diluted 
in reservoirs, lakes, and rivers would have a lower 
volatilization flux than if it were in a concentrated 
form on farm soil. Also, heavier rainfall in the corn¬ 
growing region could increase scavenging of the 
chemical from the atmosphere, thereby leaving less 
available for long-range transport. 


1.3.3 Atrazine in Tributaries 

Eleven tributaries to Lake Michigan were sampled 
from April 4, 1995 through October 31, 1995. A total 
of 108 filtered samples were collected. Most tributary 
samples contained detectable levels of atrazine, 
DEA, and DIA. The tributary samples were collected 
for purposes of estimating loadings of atrazine to the 
lake. However, the load estimates are believed to be 
low, and consequently, alternative tributary loadings 
were estimated based on watershed run-off 
algorithms using the amount of atrazine applied and 
a watershed export factor of 0.6% for the MICHTOX 


29 

































r 


and LM2-Atrazine models. For LM3, the United 
States Geological Survey (USGS) provided loadings 
that they calculated from flow and concentration data. 
However, for a 90-day period in the spring months, 
loadings were enhanced to make up for a “lost” load 
(please see section 5.3.3.3.1 in Part 5 for more 
information). Because the concentration data were 
not directly used in the models, only a brief 
description of the data will be presented here. For a 
more complete description of these data, please refer 
to Brent et al. (2001). 

1.3.3.1 Sampling and Analytical Methodology 

Samples were collected as near to river mouths as 
possible without being subject to flow reversals 
where lake water moves up the river. Composites 
were collected using the USGS quarter-point 
sampling procedure. In this procedure, the river is 
visually divided into three equal flow areas. The 
midpoint of each flow panel is sampled at 0.2 and 0.8 
times the depth. All samples were pumped and 
composited using a peristaltic pump through a 0.7 
pm glass fiber filter. The filtrate was passed through 
a 250 g, XAD-2 resin to trap the dissolved atrazine. 
Chilled samples were then taken to the analytical lab. 
Analyses were conducted using gas chromatography 
coupled to a mass spectrometer. Full details of the 
analytical methods have been published in the 
Methods Compendium (U.S. Environmental 
Protection Agency, 1997a; 1997b). 

1.3.3.2 Results 

Since tributary samples were only collected over a 
seven-month period, full seasonal trends could not 
be assessed. For the three tributaries with the 
highest mean concentrations of atrazine (St. Joseph 
River, Kalamazoo River, and the Grand River), peaks 
in atrazine concentrations occurred in mid- to late- 
May. Spring peaks were also observed for the 
degradation products DIA and DEA. 

Individual atrazine concentrations measured in the 
streams ranged from a low of 0.5 ng/L in the Pere 
Marquette River to 2,700 ng/L in the St. Joseph 
River. Mean concentrations of atrazine in the 
tributaries ranged from 3.7 in the Manistique River to 
350 ng/L in the St. Joseph River. Per Brent et al. 
(2001), these concentrations are comparable to 


concentrations measured elsewhere in the Great 
Lakes region. Eighty-six percent of the tributary 
samples contained less than 100 ng/L of atrazine, 
and all samples above 100 ng/L were in the St. 
Joseph, Kalamazoo, or Grand Rivers. Tributaries 
with the lowest mean atrazine levels were located in 
the northern portions of the lake, where land use is 
less dominated by agriculture. 

Atrazine degradation in the watershed can be 
assessed by looking at the degradation products. 
DEA and DIA concentrations correlated well with 
atrazine concentrations in tributary water samples 
(Brent et al., 2001). As atrazine concentrations 
increased, both the DEA and DIA increased. The 
ratio of concentrations of [DEA]/[atrazine] or DAR is 
often used to assess the extent of atrazine 
degradation in a sample. Ratios on individual 
measurement pairs ranged from 0.08 to 3.7, and the 
median was 0.77. Mean DARs were above 1.0 at the 
Pere Marquette, Sheboygan, and Milwaukee Rivers, 
and were significantly higher than the mean ratios at 
the Kalamazoo, Manistique, Grand, and St. Joseph 
Rivers. For all samples, the mean DAR of 1.4 
measured in October was significantly greater than 
the mean ratios in April (0.75), May (0.63), and June 
(0.87). It is common to find that the ratios increase 
for a given tributary as the time since application of 
atrazine increases. Thurman etal. (1994) also found 
an increase in DAR from <0.1 shortly after atrazine 
application to 0.4 measured later in the year. As the 
atrazine resides in the soil, processes (both biotic 
and abiotic) are operative that degrade the chemical. 
Run-off from these fields will reflect the composition 
of DAR in the soil. Furthermore, during dry spells in 
the late summer, groundwater can make up a 
significant percentage of the total flow of a river. 
Groundwater is often associated with high DARs. In 
July-August 1991, Pereira and Hostettler (1993) 
found that the DAR for Mississippi River water was 
relatively constant at 0.2 for the entire river. This 
suggests that during the travel time from Minneapolis 
to New Orleans (45-65 days), the DAR showed no 
evidence of degradation. However, in October- 
November, 1991, they found that the DAR in the river 
was 0.6 in the upper reaches of the river. The low 
DAR is believed to be associated with more run-off in 
July and August. During the fall period, the river was 
near base flow in the upper river. During base flow, 
most of the river flow is due to groundwater. DARs 


30 



measured in groundwater impacted by infiltration 
through an agricultural soil matrix are often high, and 
exceed or are close to unity (Ma and Spalding, 
1997). 

1.3.4 Atrazine in Lake Water 

1.3.4.1 Sampling and Analytical Methodology 

Open-lake water column samples were collected 
during six cruises from April 25, 1994 to April 17, 
1995. Open-lake samples were collected from 35 
sampling locations on Lake Michigan, two sampling 
locations in Green Bay, and one sampling location on 
Lake Huron (see Figure 1.1.2). The Lake Huron 
samples were collected to characterize a model 
boundary condition. Samples were collected at 
depths ranging from 1 to 257 m. During stratification, 
samples were collected at mid-epilimnion and mid- 
hypolimnion, and master stations were sampled at 
one meter below the surface and two meters off the 
bottom. During non-stratification, samples were 
collected at mid-water column depths, one meter 
below the surface, and two meters off the bottom. 

Water samples were collected using a General 
Oceanics (Model 1015) rosette sampler on board the 
Lake Guardian research vessel. Water was 
transferred from individual rosette canisters to amber 
one-liter bottles and stored at 4°C until processing at 
the testing laboratory. 

Atrazine, DEA, and DIA were isolated from filtered 
water samples using 250 mg Carbopack (Supelco 
Corporation) solid phase extraction (SPE) cartridges. 
Analytes were eluted from the SPE using 7 ml of a 
90% dichloromethane and 10% methanol solution 
(vokvol), followed by 5 ml of methanol. The eluent 
was then passed through clean anhydrous sodium 
sulfate to remove excess water. Extracts were 
concentrated to <100 pL under a nitrogen gas 
stream. Analysis of atrazine, DEA, and DIA was 
conducted using gas chromatography coupled to a 
mass spectrometer detector. Further details of the 
analytical methods can be found in the methods 
compendium (U.S. Environmental Protection Agency, 
1997a; 1997b). 


1.3.4.2 Results 

1.3.4.2.1 Spatial Variation 

A total of 234 samples (including Green Bay and the 
northern Lake Huron boundary condition samples) 
were collected and analyzed for atrazine, DEA, and 
DIA. All lake samples contained levels of atrazine 
and DEA above the MDL. All but 12 samples 
contained DIA above the MDL for that parameter. 
MDLs computed were 1.25 ng/L for atrazine, 2.46 
ng/L for DEA, and 8.27 ng/L for DIA. Skewness 
characterizes the degree of asymmetry of a 
distribution. Positive skewness indicates a 
distribution with an asymmetric tail extending towards 
more positive values. In a normal distribution, 
skewness is approximately zero. A statistical 
analysis of all lake data indicated that atrazine 
skewness equaled 0.145. To further evaluate the 
skewness for atrazine, the following analysis was 
performed (Tabachnick and Fidell, 1996). 

Skewness values of two standard errors of skewness 
( ses) or more (regardless of the sign) are probably 
skewed to a significant degree. The ses for atrazine 
can be estimated by: 

ses = v/6/7? = 0.144 

where, n = total number of open Lake Michigan 
values including duplicates and triplicates (excludes 
Green Bay and the northern Lake Huron stations) = 
288 

2(ses) = 0.2886 

Since the skewness for the atrazine lake data, 0.145, 
is less than 2 x ses, the distribution can be assumed 
to be normal. The deviation from zero can be 
assumed to be to chance fluctuation. 

Within Lake Michigan (excluding Green Bay and 
northern Lake Huron stations), lateral and vertical 
atrazine concentrations were relatively consistent 
during the LMMBP (Brent et at., 2001). Individual 
sample results ranged from 22.0 to 58.0 ng/L, and 
sampling station mean atrazine concentrations only 
ranged from 33.0 to 48.0 ng/L. Similar patterns of 
consistency among sampling stations were observed 


31 




for DEA and DIA concentrations. Atrazine 
concentrations in southern Green Bay were 
significantly higher than atrazine concentrations at 18 
Lake Michigan sampling stations. Due to the spatial 
consistency of atrazine, DEA, and DIA 
concentrations within Lake Michigan, lake-wide mean 
concentrations can be calculated to reliably represent 
the lake. Schottler and Eisenreich (1994) also found 
Lake Michigan (excluding Green Bay) to lack vertical 
and lateral gradients in the 1991 and 1992 data. It is 
not surprising that no vertical gradients were found, 
because most of the samples collected for the 
LMMBP were collected during times of non¬ 
stratification of the lake. Lake-wide concentrations 
from the LMMBP study (April 1994-April 1995) and 
previous studies are summarized in Table 1.3.3. A 
graphical representation of concentrations observed 
in 1994 is depicted in Figure 1.3.3. 

1.3.4.2.2 Seasonal Variation 

Open-lake atrazine concentrations were measured 
during six sampling cruises. Brent et al. (2001) 
concluded that statistically significant mean open- 
lake concentrations of atrazine, DEA, and DIA 
increased during the one-year LMMBP sampling 
campaign (1994-1995). Schottler and Eisenreich 
(1997) found that 1992 atrazine concentrations in the 
lake were statistically higher than the mean lake 
concentration measured in 1991. Based on these 
field measurements, it appears that the lake is 
accumulating atrazine over time. More information 
on this accumulation will be discussed in the 
modeling chapters. 


Table 1.3.3. Summary of Historical Atrazine, 
DEA, and DIA Concentrations in Lake Michigan 


Year 

Atrazine 

(ng/L) 

DEA 

(ng/L) 

DIA (ng/L) 

1991 

35 (2.0) r 

16 3 

Not Available 

1992 

37 (1.8) 1 

24 3 

Not Available 

4/1994- 
4/1995 2 

38.1 

25.8 

14.9 


Schottler and Eisenreich, 1997 

2 Brent et al., 2001 

3 Schottler and Eisenreich, 1994 

*Values are means with the standard deviation in 

parenthesis. 



Figure 1.3.3. Atrazine concentrations in Lake 
Michigan, 1994. 


References 

Banks, P.A. and D. Tierney. 1993. Biological 
Assessment of Atrazine and Metolachlor in 
Rainfall. Ciba Plant Protection Department, Ciba- 
Geigy Corporation, Greensboro, North Carolina. 
Technical Paper 1-1993, 16 pp. 

Brent, R.N., J. Schofield, and K. Miller. 2001. 
Results of the Lake Michigan Mass Balance 
Study: Atrazine Data Report. U.S. 

Environmental Protection Agency, Great Lakes 
National Program Office, Chicago, Illinois. 
EPA/905/R-01/010, 92 pp. 


32 













Foreman, W.T., M.S. Majewski, D.A. Goolsby, F.W. 
Wiebe, and R.H. Coupe. 2000. Pesticides in the 
Atmosphere of the Mississippi River Valley, Pail 
II - Air. Sci. Total Environ., 248(2):213-216. 

Goolsby, D.A., E.M. Thurman, M.L. Pomes, and 
W.A. Battaglin. 1993. Occurrence, Deposition, 
and Long Range Transport of Herbicides in 
Precipitation in the Midwest and Northeast United 
States. In: D.A. Goolsby, L.L. Boyer, and G.E. 
Mallard (Eds.), Selected Papers in Agricultural 
Chemicals in Water Resources of the 
Midcontinental United States, pp. 75-89. U.S. 
Geological Survey, Denver, Colorado. Open File 
Report 93-418, 89 pp. 

Goolsby, D.A., E.A. Scribner, E.M. Thurman, M.L. 
Pomes, and M.T. Meyer. 1995. Data on 
Selected Herbicides and Two Triazine 
Metabolites in Precipitation of the Midwestern 
and Northeastern United States, 1990-1991. 
U.S. Geological Survey, Lawrence, Kansas. 
Open File Report 95-469, 341 pp. 

Goolsby, D.A., E.M. Thurman, M.L. Pomes, M.T. 
Meyer, and W.A. Battaglin. 1997. Herbicides 
and Their Metabolites in Rainfall: Origin, 
Transport, and Deposition Patterns Across the 
Midwestern and Northeastern United States, 
1990 - 1991. Environ. Sci. Technol., 31 (5):1325- 
1333. 

Lerch, R.N., P.E. Blanchard, and E.M. Thurman. 
1998. Contribution of Hydroxylated Atrazine 
Degradation Products to the Total Atrazine Load 
in Midwestern Streams. Environ. Sci. Technol., 
32(1 ):40-48. 

Ma, L. and R.F. Spalding. 1997. Herbicide 
Persistence and Mobility in Recharge Lake 
Watershed in York, Nebraska. J. Environ. Qual., 
26(1): 115-125. 

Miller, S.M. 1999. Spatial and Temporal Variability 
of Organic and Nutrient Compounds in 
Atmospheric Media Collected During the Lake 
Michigan Mass Balance Study. M.S. Thesis, 
Department of Civil, Structural, and 
Environmental Engineering, State University of 
New York, Buffalo, New York. 181 pp. 


National Climatic Data Center. 2000. Archive of 
Climate Data. Available from the National 
Oceanic and Atmospheric Administration at 
http://www.ncdc.noaa.gov. 

Nations, B.K. and G.R. Hallberg. 1992. Pesticides 
in Iowa Precipitation. J. Environ. Qual., 
21 (3):486-492. 

Peck, A.M. and K.C. Hornbuckle. 2005. Gas-Phase 
Concentrations of Current-Use Pesticides in 
Iowa. Environ. Sci. Technol., 39(9):2952-2959. 

Pereira, W.E. and F.D. Hostettler. 1993. Nonpoint 
Source Contamination of the Mississippi River 
and Its Tributaries by Herbicides. Environ. Sci. 
Technol., 27(8):1542-1552. 

Scholtz, M.T., A.C. McMillan, C. Slama, Y. Li, N. 
Ting, and K. Davidson. 1997. Pesticide 
Emissions Modeling-Development of a North 
American Pesticide Emissions Inventory. 
Canadian Global Emissions Interpretation Centre, 
Ortech Corporation, Mississauga, Ontario, 
Canada. Final Report #CGEIC-1997-1,242 pp. 

Schottler, S.P. and S.J. Eisenreich. 1994. 
Herbicides in the Great Lakes. Environ. Sci. 
Technol., 28(13):2228-2232. 

Schottler, S.P. and S.J. Eisenreich. 1997. Mass 
Balance Model to Quantify Atrazine Sources, 
Transformation Rates, and Trends in the Great 
Lakes. Environ. Sci. Technol., 31 (9):2616-2625. 

Sweet, C.W. and K.S. Harlin. 1998. Atmospheric 
Deposition of Atrazine to Lake Michigan. 
Presented at the Air and Waste Management 
Association’s 91st Annual Meeting and 
Exhibition, June 14-18, 1998, San Diego, 
California. Illinois State Water Survey, 
Champaign, Illinois. Report Number98-TA37.02. 

Tabachnick, B.G. and L.S. Fidell. 1996. Using 
Multivariate Statistics, Third Edition. Harper 
Collin Publishers, Incorporated, New York, New 
York. 


33 



Thurman, E.M., M.T. Meyer, M.S. Mills, L.R. 
Zimmerman, C.A. Perry, and D. A. Goolsby. 
1994. Formation and Transport of 
Deethylatrazine and Deisopropylatrazine in 
Surface Water. Environ. Sci. Technol., 
28(13):2267-2277. 

Thurman, E.M. and E. Cromwell. 2000. 
Atmospheric Transport, Deposition, and Fate of 
Triazine Herbicides and Their Metabolites in 
Pristine Areas at Isle Royale National Park. 
Environ. Sci. Technol., 34(15):3079-3085. 

U.S. Environmental Protection Agency. 1997a. Lake 
Michigan Mass Balance Study (LMMB) Methods 
Compendium, Volume 1: Sample Collection 
Techniques. U.S. Environmental Protection 
Agency, Great Lakes National Program Office, 
Chicago, Illinois. EPA/905/R-97/012a, 1,440 pp. 


U.S. Environmental Protection Agency. 1997b. Lake 
Michigan Mass Balance Study (LMMB) Methods 
Compendium, Volume 2: Organic and Mercury 
Sample Analysis Techniques. U.S. 
Environmental Protection Agency, Great Lakes 
National Program Office, Chicago, Illinois. 
EPA/905/R-97/012b, 532 pp. 

U.S. General Accounting Office. 1993. Report to the 
Chairman, Subcommittee on Oversight of 
Government Management, Committee on 
Governmental Affairs, U.S. Senate: Pesticides - 
Issues Concerning Pesticides Used in the Great 
Lakes Watershed. U.S. General Accounting 
Office, Washington, D.C. GAO/RCED-93-128, 
39 pp. 


34 



PARTI 


INTRODUCTION 


Appendix 1.3.1 Information Management 

David A. Griesmer 

Computer Sciences Corporation 

Large Lakes Research Station 

9311 Groh Road 

Grosse lie, Michigan 48138 

and 

Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects 
Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research 
Branch 

Large Lakes Research Station 

9311 Groh Road 

Grosse lie, Michigan 48138 

To support the modeling efforts of the Lake Michigan 
Mass Balance Project (LMMBP), samples were 
collected and analyzed by the United States 
Geological Survey (USGS) and several universities 
(Table A1.3.1). The focus group acronyms in the 
table provide a unique identifier of data sets. The 
first two letters stand for the organization, the third 
letter represents the media sampled (air, lake, or 
tributary), and the fourth letter identifies the chemical 
(atrazine) analyzed. Project data were sent to the 
United States Environmental Protection Agency 
(USEPA) Great Lakes National Program Office 
(GLNPO) in Chicago, Illinois. GLNPO staff, under 
the direction of Louis Blume, were responsible for 
quality assurance (QA) assessment, organization, 
and consolidation of all data. To facilitate the QA 


assessment process, a SAS application, the 
Research Data Management and Quality Assurance 
System (RDMQ), developed by Syd Allen, a private 
contractor, was used to automate the QA process 
(Sukloff et at., 1995). RDMQ is a menu-driven SAS 
program. It has capabilities for loading data, applying 
quality control (QC) checks, adding validity flags, 
viewing and editing data, producing user-defined 
tables and graphs, and exporting data in ASCII files. 
These tasks are performed through a set of menu- 
driven SAS programs and macros. Data which had 
been put through the assessment process and 
approved for release by both GLNPO and the 
Principal Investigator (PI) were then sent to USEPA, 
Office of Research and Development (ORD)/National 
Health and Environmental Effects Research 
Laboratory (NHEERL)/Large Lakes and Rivers 
Forecasting Research Branch (LLRFRB)/Large 
Lakes Research Station (LLRS) for use by the 
modeling staff. 

A1.3.1.1 Overview of Information 
Management at the LLRS 

Data received from GLNPO were usually in the form 
of electronic media. Data were typically E-mailed, 
but sometimes they were downloaded from GLNPO 
databases or received on CD-ROM. Data were 
reformatted by GLNPO into a form facilitating entry 
into database programs at the LLRS. Upon arrival, 
raw data were copied to the “Immb” folder on David 
Griesmer’s personal network space (“M:\” drive). In 
addition, data were imported into one of several 
Microsoft Access databases in the “\Access\lmmb” 
folder on Mr. Griesmer’s “M:\” drive. The “M:\” drive 
was used to facilitate data security because this file 


35 



Table A1.3.1 List of Parameters Analyzed and Principal Investigators for the LMMBP Atrazine 
Modeling 


Parameter 

Focus 

Group 

Media 

Notes 

Principal Investigator 

Atrazine, Deethylatrazine 
(DEA), 

Deisopropylatrazine 
(DIA), Terbuthylazine 

IUAA 

Atmospheric Vapor 
and Particulate 

Phase, Precipitation 

Sleeping Bear 
Dunes site only. 
Keri 

Hornbuckle, U. 
of Iowa, used 
these data to 
calculate 
loadings 

Ronald Hites, Indiana 
University 

Atrazine, DEA, DIA, 
d5-Atrazine 

WSAA 

Atmospheric Vapor 
and Particulate 

Phase, Precipitation 

All stations 
except Sleeping 
Bear Dunes 
site. Keri 
Hornbuckle, U. 
of Iowa, used 
these data to 
calculate 
loadings 

Clyde Sweet, Illinois 

State Water Survey 

Atrazine 

RULA 

Open-lake 


Steven Eisenreich, 
Rutgers University 

Atrazine 

RUTA 

Tributary 

David Hall, 

USGS, used 
these data to 
calculate 
loadings 

Steven Eisenreich, 
Rutgers University 

Flow 

N/A 

Tributary 


David Hall, USGS 


space is backed up regularly and is available only to 
Mr. Griesmer. At some point in the future, the 
location of these data may change; however, limited 
access and backups of the data will be maintained. 
Data were placed in the Microsoft Access databases 
to facilitate data review/assessment and later 
retrieval for the modeling team. 

Prior to use, several reviews were done of the data 
received to look for errors in the data sets. At the 
LLRS, this review was broken up into two parts. 
First, an initial review was made to check for 
completeness of information; to look for transcription, 


programming, and formatting errors; and to review 
comments added by collection and analysis 
personnel. Second, a review was done by the data 
users to determine if the data made environmental 
sense. This type of review was conducted for the 
open-lake data. Tributary atrazine loadings and 
atmospheric atrazine fluxes/loadings did not go 
through this review process at the LLRS, but they 
were assessed by study members assigned with 
providing these loading values. Tributary atrazine, 
deisopropylatrazine (Dl A), and deethylatrazine (DEA) 
loading assessments were done by David Hall, 
(USGS). All atmospheric atrazine loading/- 


36 







concentration data were assessed by Keri 
Hornbuckle, University of Iowa. 

Samples that GLNPO determined had failed the 
RDMQ QA process were flagged with the value of 
-9999 in the Grosse He database. GLNPO preserved 
all of the values in the data sets that were received 
and flagged the analytical remark field for that 
parameter. Flagging these values as -9999 
facilitated processing by analytical software such as 
IDL. In addition, parameter values with analytical 
remark flags of “INV” (invalid data, as determined by 
the GLNPO QA evaluation), and “NAI” (no result 
reported - interference) were changed to -9999. 
Samples with the analytical remark flag of “LAC” (no 
results reported, laboratory accident) were removed. 

Documentation associated with the data was 
reviewed. RDMQ data warning fields 
(RS_NMAND,RS_WARN, RS_UPDAT) were 
checked to verify that there were no problems 
flagged by RDMQ which were inadvertently included 
in the database. Every routine field sample (RFS) 
and field duplicate (FD#) was checked to verify that 
a valid station name, sampling date, and depth 
collection information were included. The value 
ranges (minimum, maximum, average) for atrazine 
and its degradation products (DEA and DIA) were 
checked to look for any obvious errors. Data ranges 
of all data were also checked for obvious errors. 
Data were checked to verify units and to confirm 
whether blank, dilution, or surrogate correction were 
done. Sample QC and station comment fields 
(RECSTAT, RECSTATF, and STNNOTES) were 
checked for any comments associated with a sample. 
All of this information was recorded on a Data 
Verification Checklist (Table A1.3.2). If questions or 
errors were found, they were referred back to 
GLNPO for resolution. 

Upon completion of this initial data check, readme 
files were created to describe the data, and the raw 
data set(s) and readme files were copied to a data 
archive on the LLRS Unix systems. This archive is 
located at \usr\lmmbdata on the Unix servers and is 
available to modeling staff at the LLRS. Each study 
has its own directory (LMI0001-LMI0028) within the 
Immbdata archive. The directories related to the 
atrazine modeling can be found in Table A1.3.3. 


Information on other LMMBP directories can be 
found in the LMMBP PCB report (Rossmann, 2006). 

At the same time, information about data received 
(metadata) was stored in a searchable Microsoft 
Access database. The database is found on the 
LLRS common drive “\\giord2\grlcommon”, which is 
also known as the “L:\” drive. This database is 
named “Imtrack2000.mdb” and is found in the 
L:\Public\Access\lmmb folder. This database is 
available to all staff. This database can be searched 
by library number (consecutive number assigned 
when data are logged in, corresponds to LMI folder 
name in Immbdata archive), PI, parameter, PI and 
parameter, or library number and parameter (Table 
A1.3.3). 

After initial review of a data set was completed, data 
were retrieved from the Microsoft Access databases 
and exported into files (usually Microsoft Excel) for 
assessment by the modeler who would be using the 
data set. Atrazine data were assessed by William 
Richardson. Initially, only routine field samples and 
field duplicates were given to the data assessors. If 
issues or problems were found, the person assessing 
the data would then request additional QA data. If 
questions/problems could not be resolved by looking 
at QA data, they were referred back to GLNPO for 
resolution. GLNPO was informed whenever we 
rejected data. 

After the assessment process was completed, files 
were created which could be used in IDL, which is a 
software package used for visualization and analysis 
of LMMBP data. A standard format was developed 
for water data (Table A1.3.4). All files were fixed 
format ASCII text files. One of the principal uses of 
IDL was to develop volume-weighted averages 
(VWA) estimates of parameter concentrations for 
each cell in the modeling grid. These VWA estimates 
could then be compared to model results. 

A 1.3.1.2 Summary 

The LMMBP data received at the LLRS were 
carefully evaluated prior to use to ensure that the 
field data being used by the modelers were as 
accurate as possible. In addition, data were archived 
and cataloged to protect these valuable data sets 






Table A1.3.2. Example of Data Verification Checklist Used for the LMMBP 


Data Verification Checklist 

FOCUS_ Version Number_ Date Received_ 

Description:___ 

1. Read any documentation which came with data files:______ 

2. Make sure I understand field names in RDMQ files:_ 

3. Check fields which according to RDMQ should not be flagged/or indicate some question, with data (e.g. 
RS_NMAND, RS_WARN, RS_UPDAT). 

RS_NMAND_ 

RS_WARN_ 

RSJJPDAT_ 

4. Make sure every RFS and field duplicate has station, date, depth collected information. 


5. Check to make sure every sample has station name that is valid. 


6. Check number of RFS and field duplicates for every analyte. Total Samples __ 


Analyte 

RFS 

FDn 

Analyte 

RFS 

FDn 

Analyte 

RFS 

FDn 

Analyte 

RFS 

FDn 

Analyte- 

RFS 

FDn 

Analyte 

RFS 

FDn 


38 





































7. Analysis Results for RFS and field duplicates for every analyte. 


Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avg 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avg 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avg 

Min 

Max 

Count 

Analyte 

Avq 

Min 

Max 

Count 

Analyte 

Avg 

Min 

Max 

Count 


39 












































































































8. Check date ranges of data to see if they are reasonable. 


Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 

Analyte 

Min 

Max 


9. Check to verify units information looks alright. 


10. Number of significant digits for each analyte. 


11. Number of negative values for each analyte. 


12. Check flags on RFS and field duplicates. 


40 

















































13. Core slice range (sediment)/species, age, length, weight (fish). 


14. Check blank correction, dilution, and surrogate correction fields. 


15. Questions about QC Coordinator remarks (RECSTAT). Check flags for whole record (RECSTATF). 
Questions about Station Notes (STNNOTES), Field Remarks (FREMARK), and Sample Description 
(SAMPDESC). 


16. Additional Questions. 


41 


























Table A1.3.3. Printout of Information Stored in the LMMBP Tracking Database Related to Atrazine 
Modeling (L:\Public\Access\lmmb\lmtrack2000.mdb) 



LMMBP DATA ARCHIVE - QUICK REPORT. Note: All Data Archived on 

superior.grl.epa.gov in /usr/lmmbdata. 

Library No. 

LM10001 PI: David Schwab 

Description 

Hourly Lake Michigan wind, wave, and atmospheric data (5 km grid) for 1982, 1983, 1994, 1995. 
Original data files were converted to SEDZL and POM formerly by M. Settles. Also, bathymetric data 
for Lake Michigan. 

Library No. 

LM10002 PI: William Richardson 

Description 

STORET conventional and general chemistry data for Lake Michigan, April 1962-August 1993. Note: 
Date range varies by parameter, includes original file, reformatted spreadsheet, and MS Access file. 

Library No. 

LM10003 PI: David Schwab 

Description 

Two-dimensional and three-dimensional GLERL hydrodynamics data for the Lake Michigan 5 km grid. 
2D data: January 1982-September 1983; 3D: covers January-July 1982. Program//llrssrv2 
/~model/dev/PATRIC2D/RCS is for 2D processing, no three-dimensional programming yet. 

Library No. 

LM10004 PI: Steven Eisenreich 

Description 

Open-lake (RULA) and tributary (RUTA), atrazine, DEA, DIA data for the LMMBP. Open-lake 325 
samples (1 /17/94-4/17/95). Tributary: 126 samples (4/4/95-5/15/96). Revised version of data sent 
2/19/98. 

Library No. 

LM10005 PI: Angela Bandemehr 

Description 

Hourly meteorological data (air temperature, solar radiation, relative humidity, wind speed and direction, 
and precipitation) from 13 air sampling sites both in and outside of the Lake Michigan basin. 11/30/90- 
12/31/96 (Dates vary by site). 

Library No. 

LM10006 PI: Glenn Warren 

Description 

Seabird water temperature data for seven LMMBP surveys, April 1994-October 1995. Data collected 
at 0.5 m intervals. Does not include January 1994 survey. Note: Data received was extensively 
revised from original version. 

Library No. 

LM10007 PI: David Hall 

Description 

Tributary flow data for 11 tributaries to Lake Michigan (Fox, Grand, Indiana Harbor, Kalamazoo, 
Manistique, Menominee, Milwaukee, Muskegon, Pere Marquette, Sheboygan, St. Joseph), 1/1/94- 
12/31/95. Some data estimated. 

Library No. 

LM10011 PI: David Schwab 

Description 

Lake Michigan final report, hourly circulation, meteorology, and wave data (5 km grid) for 1982, 1983, 
1994, 1995. Includes intake, cruise, mooring, water level data. Also, HTML files and images, model 
results (XDR format), Fortran and IDL programs. 

Library No. 

LM10020 PI: Keri Hornbuckle 

Description 

Atmospheric atrazine and nutrient (N0 3 , total phosphorus, TKN) wet deposition loading data for Lake 
Michigan 5 km grid cells used in hydrodynamic model. Atrazine wet deposition and particulate monthly 
concentration data. Data for 10/94-10/95 (nutrient) and 5/94-10/95 (atrazine). 


42 












Table A1.3.3. Printout of Information Stored in the LMMBP Tracking Database Related to Atrazine 
Modeling (L:\Public\Access\lmmb\lmtrack2000.mdb) (Continued) 


Library No. LM10022 PI: David Hall 

Description Atrazine, DEA, DIA tributary loading data for 11 monitored tributaries and atrazine data for unmonitored 

_ tributaries to Lake Michig an. Data covers the time period: 1/1/94-12/31/95. 

Library No. LM10026 PI: Nathan Hawley 

Description Current velocity, water transparency, temperature from three stations, 10/31/94-10/11/95. In situ 
sediment resuspension from sediment flume experiments (8/12/95-9/23/98). Also profile data - 
temperature, dissolved oxygen, conductivity, BAT, pH, fluorescence, TSM data from six stations in 
Lake Michigan (1/4/95-11/29/95). 

Library No. LM10027 PI: Barry Lesht 

Description Current velocity and direction, bottom wave orbital velocity, temperature, beam attenuation, and TSM 
data collected from Tripod Station 98 (latitude 42 52.18, longitude 87 42.41), during the EEGLE project, 
4/2/98-12/1/98. Data collected every 30 minutes. 

Library No. LM10028 PI: Michael Settles 

Description NEMA and NOAA wind speed and direction, wave height and period data for six stations in Lake 
Michigan, retrieved from USACOE Web Site (http://bigfoot.wes.army.mil/c300.html). 1980-1998 (not 
all stations cover entire date range). NEMO-Daily data, NOAA-Hourly data. 


Table A1.3.4. Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs 


Beginning - 
Ending Columns 

Variable Description 

Format (A = Alpha, F 
= Floating Point No., 1 
= Integer, X = Skip) 

Sort Order (A = 
Ascending, D = 
Descending, 
Blank = None) 

Missing Data 
Code 

1 - 7 

Cruise Name 

A7 

A 

Blank 

8-8 

Blank Space 

IX 

N/A 

N/A 

9 - 14 

Latitude (ddd.ddd) 

F6.3 


Blank 

15 - 15 

Blank Space 

IX 

N/A 

N/A 

16-22 

Longitude (-ddd.ddd) 

F7.3 


Blank 

23 - 23 

Blank Space 

IX 

N/A 

N/A 

24 - 35 

Station Name 

A12 

A 

Blank 

36-36 

Blank Space 

IX 

N/A 

N/A 

37 - 44 

Depth Sampled 

F8.0 

A 

Blank 

45 - 45 

Blank Space 

IX 

N/A 

N/A 


43 
























Table A1.3.4. Generalized Format for the LMMBP Water Data to be Analyzed With IDL Programs 
(Continued) 


Beginning - 
Ending Columns 

Variable Description 

Format (A = Alpha, F 
= Floating Point No., 1 
= Integer, X = Skip) 

Sort Order (A = 
Ascending, D = 
Descending, 
Blank = None) 

Missing Data 
Code 

46-53 

Sampling Start Date 
(mm/dd/yy) 

A8 

A 

Blank 

54 - 54 

Blank Space 

IX 

N/A 

N/A 

55 - 58 

Sampling Start Time (24 
hour clock) 

A4 


Blank 

59 - 59 

Blank Space 

IX 

N/A 

N/A 

60 - 67 

Sampling End Date 
(mm/dd/yy) 

A8 

A 

Blank 

68 - 68 

Blank Space 

IX 

N/A 

N/A 

69 - 72 

Sampling End Time (24 
hour clock) 

A4 


Blank 

73-73 

Blank Space 

IX 

N/A 

N/A 

74 - 75 

Filter Fraction 

A2 

A 

Blank 

76 - 76 

Blank Space 

IX 

N/A 

N/A 

77 - 79 

Sample Type 

A3 

D 

Blank 

80 - 80 

Blank Space 

IX 

N/A 

N/A 

81 - 88 

Value Parameter 1 

F8.0 


-9999 

89-103 

Parameter 1 Flags 

A15 


Blank 

104-111 

Value Parameter 2 

F8.0 


-9999 

112 - 126 

Parameter 1 Flags 

A15 

i t 

Blank 


1 





▼ 





Value Parameter n 

F8.0 


-9999 


Parameter n Flags 

A15 


Blank 


44 





























and make it easier for users to find the information. 
Incorporation of this information into LLRS Microsoft 
Access databases has given us flexibility in retrieving 
the information needed by the modeling staff at the 
LLRS. 

References 

Rossmann, R. (Editor). 2006. Results of the Lake 
Michigan Mass Balance Project: Polychlorinated 
Biphenyl Modeling Report. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and Environmental 
Effects Research Laboratory, Mid-Continent 
Ecology Division-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. EPA/600/R- 
04/167, 579 pp. 


Sukloff, W.B., S. Allan, and K. Ward. 1995. RDMQ 
User Manual. Environment Canada, Atmospheric 
Environment Service, North York, Ontario, 
Canada. 91 pp. 








PART 1 


INTRODUCTION 


Chapter 4. Representativeness of the 
Lake Michigan Mass Balance Project 
(LMMBP) Years Relative to Lake 
Michigan’s Historic Record 

Ronald Rossmann, Kenneth R. Rygwelski, and 
Russell G. Kreis, Jr. 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects 
Research Laboratory 
Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research 
Branch 

Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 
and 

Gregory J. Gerstner, Xiaomi Zhang, and Brent 
Burman 

Welso Federal Services, LLC 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 

1.4.1 Introduction 

A major concern related to modeling contaminants in 
the lake was the representativeness of the years of 
sampling (1994-1995) relative to the historical record. 
This was particularly important when using the 
models to predict future conditions in the lake. The 
LM2-Atrazine and LM3-Atrazine models used 
hydrodynamic model output from 1994-1995 in their 
construct (see Parts 4 and 5 of this report, 


respectively, for more information). In addition, 
atrazine loading estimates for any given year can be 
affected by various meteorological conditions (see 
Part 2, Chapters 2 and 3 of this report). If these data 
did not represent something close to average 
conditions, the resulting predictions could be biased. 
Parameters considered most important to the 
performance of the atrazine models included ice 
cover, air temperature, water temperature, lake water 
levels, precipitation, tributary flows, wind, and waves. 
Potential impacts on the various models are 
discussed below. Each of these were investigated 
for the representativeness of the 1994-1995 project 
data relative to the available historical data record. 

1.4.2 Ice Cover 

Ice cover impacts the volatilization, absorption, and 
physical mixing of the lake during the winter months. 
In locations where there is ice cover, gas exchange 
between the water and atmosphere is prevented by 
the physical barrier. Physical mixing includes not 
only the mixing of the water column, but also the 
interaction of waves with the lake bottom to 
resuspend sediments. Winters having extensive ice 
cover yield a more poorly mixed water column, and 
a large region of the lake becomes depositional due 
to the lack of wave resuspension of sediments. Once 
ice retreats in the spring, sediments accumulated 
during ice cover will be resuspended as a pulse. Ice 
cover can cause significant changes in winter 
circulation patterns in a large lake (Campbell et al., 
1987). The years of interest were 1982, 1983, 1994, 
and 1995. The hydrodynamic modeling included 
three-dimensional lake circulation, surface flux for 
atmospheric input, and wind-wave models (Schwab 


46 





and Beletsky, 1998). These were calibrated for the 
period of 1982-1983 using temperature, current, 
water level, and wind-wave measurements. The 
calibrated model was applied to 1994-1995 and 
verified. There was no ice modeling component for 
the version of the hydrodynamic model applied. 
Thus ice cover was important for understanding any 
potential weaknesses associated with the 
hydrodynamic results as well as the dynamics of 
exchanges between the water and the atmosphere. 

Ice cover data were available from the National 
Oceanic and Atmospheric Administration (NOAA)/ 
Great Lakes Environmental Research Laboratory 
(GLERL) (Assel, 2003). This data set is partially 
described in Assel et al. (2002). Tabular information 
presented in Assel (2003) were summarized in a 
manner that seemed appropriate for this discussion 
(Table 1.4.1). For the period when ice was recorded 
on Lake Michigan, the mean and median daily ice 
cover were 16.7% and 14.7%, respectively. An ice 
year began with the first ice. For example, 1982 may 
include December of 1981. Both 1982 and 1994 
were greater than the mean and median; whereas 
1983 and 1995 were less than the mean and median. 
None of the four years represented an extreme of 
mean daily ice cover. The lowest mean daily ice 
cover was observed in 2002, and the highest was 
observed in 1977. Results for each winter’s 
maximum daily ice cover were similar to mean daily 
ice cover. Ice cover is extremely variable from year- 
to-year. The impact upon hydrodynamics as 
modeled was believed to be minimal with respect to 
1983 and 1995 when ice cover was quite low. 
Though high ice cover occurred during the winters of 
1982 and 1994, these periods were not a part of the 
hydrodynamic model period. Using the 
hydrodynamic model information for models used to 
predict future conditions could lead to potential 
errors. Modeled circulation patterns could be in error 
and impact a high bias to modeled current velocities 
during the winters of high ice cover years due to the 
lack of an ice model within the hydrodynamics model. 

1.4.3 Water and Air Temperatures 

Water and air temperature data were retrieved from 
the National Data Buoy Center (U.S. Department of 
Commerce, 2002). Data from buoy numbers 45002 


(north buoy) and 45007 (south buoy) were reviewed 
(Figure 1.4.1). Water temperature sensors were 
located 1 m below the water surface, and air 
temperature sensors were located 4 m above the 
surface. Water and air temperature data were 
available 1979 through 2002 for the north buoy and 

1981 through 2002 for the south buoy. 

Water temperature is highly variable from year-to- 
year. The data had been stratified in two ways for 
presentation. First, monthly mean temperatures 
were calculated and plotted for the south (Figure 
1.4.2) and north (Figure 1.4.3) buoys. Years of 
importance to the hydrodynamic model were 
highlighted. It was interesting to note that 1983 and 
1995 had higher monthly mean temperatures than 

1982 or 1994. Both 1983 and 1995 had above 
normal maximum mean monthly temperature; 
whereas, 1982 had a typical maximum and 1995 had 
a very low maximum. This was reflected in the 
previously discussed ice cover for the four years. 
Water temperatures tended to be higher at the 
southern buoy than at the northern buoy, reflecting its 
more southerly latitude. 

One way to identify the relative lake warming rate 
among years was to look at the mean June water 
temperature for the period of observation available 
from the NOAA buoys. Mean June temperatures at 
the south (Figure 1.4.4) and north (Figure 1.4.5) 
showed similar patterns that were quite interesting. 
Beginning in 1983, relatively high mean June 
temperatures were observed every four years (1983, 
1987, 1991,1995, 1999). This cycling, as well as the 
apparent increasing mean June water temperature 
for the period of record, should be further 
investigated. Both of these trends can impact long¬ 
term model forecasts. The years of the Lake 
Michigan Mass Balance Project (LMMBP) (1994 and 
1995) represented a fairly average mean June 
temperature and one of the relatively high means, 
respectively. 

The exchange of atrazine between the air and water 
are dependent on both water and air temperatures. 
Air temperature varied from year-to-year at the south 
and north buoys (Figures 1.4.6 and 1.4.7). Because 
air temperature drives observed water temperatures, 
it was not surprising that patterns observed and 


47 







Table 1.4.1. Summary of Lake Michigan Ice Cover Based Upon Assel (2003) 

Year 

Mean Daily 

Percent Ice Cover 
During Ice Period 

Days of 

Observed 

Ice 

Maximum 

Daily Percent Ice 
Cover 

1973 

13.3 

104 

33.0 

1974 

16.9 

122 

39.4 

1975 

13.9 

113 

28.1 

1976 

15.5 

119 

29.5 

1977 

46.5 

132 

93.1 

1978 

26.6 

132 

66.6 

1979 

35.2 

132 

92.3 

1980 

18.2 

106 

38.6 

1981 

24.6 

112 

53.8 

| 1982 

24.0 

135 

60.2 | 

I 1983 

8.2 

118 

23.6 | 

1984 

15.6 

127 

43.3 

1985 

20.1 

119 

41.3 

1986 

25.3 

126 

66.8 

1987 

9.1 

100 

19.3 

1988 

16.6 

104 

32.7 

1989 

13.1 

140 

30.9 

1990 

17.5 

132 

32.4 

1991 

10.0 

120 

21.5 

1992 

8.3 

149 

32.8 

1993 

11.0 

126 

32.2 

| 1994 

27.3 

134 

82.7 | 

I 1995 

7.2 

120 

21.6 | 

1996 

19.4 

161 

75.0 

1997 

13.4 

156 

37.8 

1998 

6.1 

109 

15.1 

1999 

8.7 

111 

23.0 

2000 

9.2 

103 

27.2 

2001 

13.4 

134 

29.5 

2002 

6.0 

116 

12.4 

Mean 

16.7 

124 

41.2 

Median 

14.7 

121 

32.8 

Minimum 

6.0 

100 

12.4 

Maximum 

46.5 

161 

93.1 


48 














Figure 1.4.1. Location of the NOAA buoys in 
Lake Michigan. 



Figure 1.4.3. Monthly mean water temperatures 
in northern Lake Michigan. 



Figure 1.4.4. Mean June water temperatures in 
southern Lake Michigan. 


south buoy 

-m- data years for 
Lake Michigan 
hydrodynamic 



1980 


2005 



1980 


i—|—i—i—i—r 

2000 2005 


Figure 1.4.2. Monthly mean water temperatures Figure 1.4.5. Mean June water temperatures in 
in southern Lake Michigan. northern Lake Michigan. 


49 



























































































































Figure 1.4.6. Monthly mean air temperatures in 
southern Lake Michigan. 



Figure 1.4.7. Monthly mean air temperatures in 
northern Lake Michigan. 


conclusions made for water temperature are the 
same for air temperature. The cyclic pattern of June 
mean water temperatures was also found for the air 
temperatures (Figure 1.4.8 and 1.4.9). As additional 
data become available, future modeling efforts will 
need to address these cyclic patterns and long-term 
temperature trends for water and air temperatures. 



Figure 1.4.8. Mean June air temperatures in 
southern Lake Michigan. 



Figure 1.4.9. Mean June air temperatures in 
northern Lake Michigan. 


most noticeable for shallow water segments and 
predictions from the hydrodynamic model and 
surface water model could be affected. Monthly 
mean lake water levels varied between 175.5 and 

177.5 m for the period of record (1918-1997). Lake 
levels during 1994 and 1995 were near the average 
for the period of record (Figure 1.4.10). 

1.4.5 Precipitation 


1.4.4 Lake Water Levels Precipitation influences the flux of airborne 

contaminants to the lake, impacts tributary loading 
Lake levels can affect model geometry. If segment rates, and controls water levels. The 1982 and 1983 
volume deviates significantly from the volumes used hydrodynamic years, and the 1994 and 1995 project 
at the time of calibration, model results can be years were compared to the previous 50 years of 
impacted. On a percentage basis, the impact will be data (Croley and Hunter, 1994). 


50 


























































































































Figure 1.4.10. Record of mean monthly water levels for Lake Michigan. 


1.4.5.1 Annual Comparisons 

Precipitation to Lake Michigan for 1982, 1983, 1994, 
and 1995 were close to the 50-year mean for the lake 
(Figure 1.4.11). 1982 and 1983 were slightly above 
the mean and 1994 and 1995 were slightly below the 
mean. 1995 total annual precipitation was very close 
to the 50-year mean for over-lake precipitation. No 
visual trend was apparent in the total annual amounts 
of precipitation over the 50-year period. 

1.4.5.2 Monthly Comparisons 

The monthly mean precipitation for 1982, 1983, 
1994, and 1995 were compared to the 50-year mean 
for the period of 1949 through 1998 (Figure 1.4.12). 
For the years of interest, January, July, November, 
and December of 1982; May of 1983; and October of 
1995 had relatively high amounts of precipitation, 
exceeding one standard deviation of the 50-year 
mean. For the four years of interest, February of 
1982; June of 1983; March, May, and December of 
1994; and June of 1995 had relatively low amounts 
of precipitation. This illustrates that, in any one year, 
precipitation varies from month-to-month while the 
precipitation for the year can be at or near the 
average expected. 


1.4.6 Tributary Flows 

Tributary flows impact the delivery of materials to the 
lake, including nutrients and contaminants. During 
high flow events triggered by spring snow melt or rain 
events, tributary flows increase and materials can be 
carried from the watersheds to the tributaries. Within 
the tributary, sediments containing contaminants may 
resuspend. Thus the fluxes of solids, nutrients, and 
contaminants to the lake have the potential to 
increase during high flow events. Tributary flows 
were obtained from the United States Geological 
Survey (USGS) website (www.usgs.gov). A historical 
average and median daily flow were calculated for 
each tributary for the period of record, as well as for 
the 1994-1995 and 1982-1983 time periods. During 
1982 and 1983, tributary flows were approximately 
20% greater than the average flow (Figure 1.4.13). 
The 1994-1995 time period had relatively ordinary 
tributary flows (Figure 1.4.14). 

1.4.7 Summary 

Lake Michigan is acted upon by a number of physical 
parameters that impact the physics, chemistry, and 
biology of the lake. For a lake the size of Lake 
Michigan, changes in these parameters can be 


51 





























CM 


Figure 1.4.11. Annual precipitation to Lake Michigan between 1949 and 1998. 



Figure 1.4.12. Comparison of 1982, 1983, 1994, and 1995 monthly mean precipitation to the mean for 
the period of 1949 through 1998. 


52 








































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5000 


4000- 


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aci 


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□ historical mean 
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o 1983 mean 


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Figure 1.4.13. Comparison of tributary flow for hydrodynamic model calibration (1982-1983) to the 
historic means. 



Figure 1.4.14. Comparison of tributary flow for the study period (1994-1995) to the historic means. 


53 




































































































significant, especially when models are used in long¬ 
term predictions to predict the outcome of various 
scenarios. The primary driving forces are wind, air 
temperature, and precipitation. These impact 
tributary flows, lake levels, waves, water circulation, 
water temperature, and ice cover. For the period of 
record, these driving forces varied from year-to-year. 
The period of 1982 to 1983 was used to calibrate the 
hydrodynamic models. Fortunately for the period of 
time the models were calibrated, conditions were not 
at any extreme. This was also true for the period of 
1994 and 1995 when the models were applied. 
However, the impact of ice cover remains a concern 
and will have to be dealt with in the future. 

Temperature can impact atrazine contaminant 
modeling. Air temperature impacts how quickly the 
lake warms in any one year. Water temperature 
impacts the volatilization of atrazine. There appears 
to be a four-year cycle of quicker warming which 
exists within a trend of general warming of the lake. 
The trend of warming may be part of a longer-term 
undocumented cycle or may be related to climate 
change. For future modeling, these cycles and 
trends will have to be considered to improve long¬ 
term predictions. 

Precipitation will impact both lake levels and tributary 
flows. In wet years, more atrazine may be delivered 
to the lake (see Part 2, Chapter 2). Precipitation was 
within the normal range for all years of modeling 
interest, resulting in lake levels and tributary flows 
that were within normal bounds. Changes in lake 
levels as well as the response of tributaries to 
precipitation events will need to be considered for 
future modeling used to predict changes of 
contaminants within the lake. 


References 

Asset, R.A., D.C. Norton, and K.C. Cronk. 2002. A 
Great Lakes Digital Ice Cover Data Base for 
Winters 1973-2000. National Oceanic and 
Atmospheric Administration, Great Lakes 
Environmental Research Laboratory, Ann Arbor, 
Michigan. NOAA Technical Memorandum ERL 
GLERL-121,46 pp. 

Assel, R.A. 2003. NOAA Great Lakes Ice Atlas. An 
Electronic Atlas of Great Lake Ice Cover. 
National Oceanic and Atmospheric 
Administration, Great Lakes Environmental 
Research Laboratory, Ann Arbor, Michigan. 

Campbell, J.E., A.H. Clites, and G.M. Green. 1987. 
Measurements of Ice Motion in Lake Erie Using 
Satellite-Tracked Drifter Buoys. National Oceanic 
and Atmospheric Administration, Great Lakes 
Environmental Research Laboratory, Ann Arbor, 
Michigan. NOAA Technical Memorandum ERL 
GLERL-30, 22 pp. 

Croley, T.E., II and T.S. Hunter. 1994. Great Lakes 
Monthly Hydrologic Data. National Oceanic and 
Atmospheric Administration, Great Lakes 
Environmental Research Laboratory, Ann Arbor, 
Michigan. NOAA Technical Memorandum ERL 
GLERL-83, 13 pp. 

Schwab, D.J. and D. Beletsky. 1998. Lake Michigan 
Mass Balance Study: Hydrodynamic Modeling 
Project. National Oceanic and Atmospheric 
Administration, Great Lakes Environmental 
Research Laboratory, Ann Arbor, Michigan. 
NOAA Technical Memorandum ERL GLERL-108, 
55 pp. 

U.S. Department of Commerce. 2002. National 
Data Buoys. National Weather Service, National 
Oceanic and Atmospheric Administration, Ann 
Arbor, Michigan. Available from National Data 
Buoy Center at http://www.ndbc.noaa.gov. 


54 




PART 1 


INTRODUCTION 


Chapter 5. Atrazine Modeling Overview 

Douglas D. Endicott 

Great Lakes Environmental Center 

Traverse City, Michigan 

and 

William R. Richardson (Retired), Ronald Rossmann, 
and Kenneth R. Rygwelski 
United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects 
Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research 
Branch 

Large Lakes Research Station 

9311 Groh Road 

Grosse lie, Michigan 48138 

1.5.1 Background 

The atrazine mass balance project was based upon 
the Enhanced Monitoring Program (EMP), a 
comprehensive, two-year synoptic survey for 
selected toxic chemicals in the Lake Michigan 
ecosystem. The atrazine EMP included tributary load 
and atmospheric deposition monitoring; ambient 
water column; and additional measurements to 
define and confirm transport and fate processes. 
The project was led by the United States 
Environmental Protection Agency (USEPA)/Great 
Lakes National Program Office (GLNPO). Modeling 
support to the project was provided by the 
USEPA/Mid-Continent Ecology Division (MED)/Office 
of Research and Development (ORD)/Large Lakes 
Research Station (LLRS) in cooperation with the 


Atmospheric Research and Exposure Assessment 
Laboratory (AREAL); the National Oceanic and 
Atmospheric Administration (NOAA)/Great Lakes 
Environmental Research Laboratory (GLERL); and 
other cooperators. The research developed a suite 
of integrated mass balance models to simulate the 
transport and fate of atrazine in Lake Michigan. 

The project directly supports the development of a 
Lake-wide Management Plan (LaMP) for Lake 
Michigan that is mandated under Section 118 of the 
1992 Clean Water Act. Atrazine and degradation 
products are on the Lake Michigan LaMP 2008 
Watch List. Chemicals on the Watch List include 
those chemicals that have the potential to impact the 
Lake Michigan ecosystem; is present in the Lake 
Michigan watershed; and has the potential for 
bioaccumulation, persistence in water or sediment, or 
toxicity singly or through synergistic effects. In a 
June 1993 response to an inquiry by U.S. Senator 
Carl Levin, the United States General Accounting 
Office (now called the United States Government 
Accountability Office [USGAO]) recommended that 
the USEPA assess the persistence of pesticides, 
such as atrazine, in the Great Lakes and to report 
their findings to the pesticides reregistration program 
(U.S. General Accounting Office, 1993). The results 
of the Lake Michigan Mass Balance Project (LMMBP) 
atrazine modeling have been forwarded to the 
reregistration program for consideration. 

1.5.2 LMMBP Modeling Objectives 

Development of effective strategies for atrazine 
management requires a quantitative understanding 
of the relationships between sources, inventories, 


55 





concentrations, and effects of atrazine in the 
ecosystem. This approach integrated load 
estimation, ambient monitoring, and research efforts 
within a modeling framework that was compatible 
with both scientific as well as ecosystem 
management objectives. The mass balance 
approach estimated the magnitude of mass fluxes 
that constitute the pathways for atrazine transport 
into and out of the lake and processes that distribute 
toxics within the lake water column. Based upon 
these estimates, the mass balance was used to 
determine the rate of change in concentrations and 
inventories of atrazine as inputs such as atmospheric 
and tributary loadings changes. Thus the mass 
balance can serve as a useful tool to estimate or 
predict the outcome of alternatives under 
consideration for toxics management. 

In general, atrazine modeling efforts associated with 
the LMMBP had the following objectives: 

1. Provide a consistent framework for integrating 
load estimates, ambient monitoring data, and 
research efforts, leading to a better 
understanding of atrazine chemical sources, 
transport, and fate in Lake Michigan. 

2. Using flow and concentration measurements, 
estimate the loading of atrazine from all major 
tributaries to Lake Michigan for the duration of 
the study. 

3. Based on county-level usage of atrazine within 
the basin, make independent estimates of 
atrazine loading to the lake via tributaries. 

4. Estimate the atmospheric deposition and air- 
water exchange of atrazine, including spatial and 
temporal variability over Lake Michigan. 

5. Calibrate and confirm mass balance models for 
atrazine using project data based upon models 
for hydrodynamic and atrazine transport and fate. 

6. Based upon the mass balance models, evaluate 
the magnitude and variability of toxic chemical 
fluxes within and between lake compartments, 
especially between the water column and the 
atmosphere. 


7. Apply the calibrated mass balance models to 
forecast atrazine concentrations in water 
throughout Lake Michigan based upon 
meteorological forcing functions and future 
loadings based upon load reduction alternatives. 

8. Predict the water concentration of atrazine in 
lake model cells receiving loads from tributaries 
contributing a relatively high percentage of the 
total tributary load to the lake. Compare these 
predictions to water quality standards. 

9. Estimate (quantify) the uncertainty associated 
with estimates of tributary and atmospheric 
loads of atrazine and model predictions of 
contaminant concentrations. 

10. Identify and prioritize further monitoring, 
modeling, and research efforts to (1) further 
reduce uncertainty and improve accuracy of 
predictions; (2) establish additional cause-effect 
linkages, such as ecological risk endpoints and 
feedbacks; and (3) evaluate additional source 
categories, such as non-point sources in the 
watershed. 

Unlike the other LMMBP-modeled toxics 
(polychlorinated biphenyls (PCBs), mercury, and 
trans- nonachlor), atrazine does not sorb to solids to 
any great extent, and it does not bioaccumulate. It 
is soluble in water and can migrate from farm fields 
to Lake Michigan via run-off events. The herbicide is 
also transported to Lake Michigan via atmospheric 
pathways. 

1.5.3 Historical Modeling 

The modeling design and approach for the LMMBP 
reflects a progression of prior modeling efforts in 
Lake Michigan and throughout the Great Lakes. 
These include eutrophication and toxic substance 
mass balance models, food web bioaccumulation 
models, and predictive hydrodynamic and sediment 
transport models. Although not a comprehensive 
review, several of these prior modeling efforts are 
discussed below. 


56 




1.5.3.1 Completely-Mixed Lakes-ln-Series Model 

A lakes-in-series model for conservative substances 
was developed by Sonzogni et al. (1983) and applied 
to forecast chloride concentrations in each of the 
Great Lakes as a function of expected future 
loadings. This model demonstrated that 
concentrations of non-reactive substances would 
substantially “lag” the history of their input. This was 
especially the case for Lake Michigan, where 
maximum chloride concentrations were not predicted 
to occur until the 22nd Century despite declining 
loads after the 1970s. Similarly strong, non-steady- 
state behavior may be expected for other chemicals 
which are non-reactive and weakly associated to 
particles. 

1.5.3.2 MICHTOX 

MICHTOX was adapted from the general model, 
WASP4 (Ambrose et al., 1988), and has served well 
as a screening-level model for Lake Michigan over 
the past several decades. An integrated mass 
balance and bioaccumulation model for PCBs 
(modeled as two homologs) and 10 other toxic 
chemicals was developed as a planning tool for the 
LMMBP (Endicott etal., 2005). The MICHTOX mass 
balance model was calibrated to suspended solids 
and plutonium data for the southern lake basin, while 
the bioaccumulation model combined Thomann and 
Connolly’s (1984) effort with chemical-specific 
parameterization from Lake Ontario. MICHTOX 
demonstrated that reasonable predictions of PCB 
concentration trends in water, sediment, and biota 
could be developed although significant uncertainties 
regarding sediment-water and air-water contaminant 
transport remained. These are the most significant 
transport fluxes for PCBs and presumably other 
hydrophobic contaminants. Major data gaps for other 
priority toxics allowed only order-of-magnitude 
estimates of load-concentration relationships. When 
this model was developed and run, available 
monitoring data for toxic chemical concentration in 
tributaries, air, lake water, sediment, and biota were 
not adequate to define loading trends or to relate the 
distribution of loadings to contaminant gradients 
observed for sediment and biota. Credible model 
predictions of toxic chemical transport, fate, and 
bioaccumulation would depend upon developing a 
comprehensive data set quantifying loadings, 


sediment inventories, concentrations, and transport 
fluxes on a spatially-resolved basis and localized 
descriptions of food web structures. 

MICHTOX was also applied to model atrazine in Lake 
Michigan and Green Bay. It was first applied prior to 
the release of LMMBP data using only historical data 
(Rygwelski et al., 1999), and it was also applied 
again after LMMBP data became available. 
MICHTOX served as a low-resolution model and the 
application is discussed in this report. 

1.5.3.3 Green Bay Mass Balance Project 

The Green Bay Mass Balance Project (GBMBP) 
demonstrated the feasibility of applying mass 
balance principles to manage toxic chemicals in the 
Great Lakes ecosystem. A two-year (1989-1990) 
synoptic sampling program was designed to collect 
appropriate and complete data for the mass balance 
study. A suite of integrated mass balance and 
bioaccumulation models were developed which, 
together, provided an ecosystem-level simulation of 
sources, transport, fate, and bioaccumulation of 
PCBs throughout the Fox River and Green Bay. 
These mass balance models were also based on the 
general WASP4 model construct. This study 
advanced the state-of-the-art of mass balance 
modeling, particularly the ability to construct a fairly 
complete and accurate description of contaminant 
mass transport. 

Several aspects of the Green Bay modeling effort 
were noteworthy. Particle transport and sorption 
processes were found to be of fundamental 
importance as bases for contaminant modeling. 
Resuspension of contaminated sediments in the Fox 
River constituted the major source of PCBs to the 
river as well as the bay. In the bay, particle sorbent 
dynamics were strongly affected by phytoplankton 
production and decay. The relative significance of 
hydraulic transport, sediment transport, burial, 
volatilization, and open-lake boundary exchange 
processes upon the PCBs mass balance varied 
considerably with location in Green Bay. 
Radionuclide tracers were again essential for 
calibration of particle fluxes and confirmation of long¬ 
term contaminant transport predictions. The 
significance of contaminant accumulation at the base 
of the food web and fish movement in relation to 


57 












exposure gradients were demonstrated in the 
bioaccumulation model. The LMMBP demonstrated 
the linked submodel approach to ecosystem model 
development and application, and the feasibility of 
using such a model for assessing the effectiveness 
of toxics management control alternatives. 

The GBMBP models were a precursor to our LM2- 
Atrazine model. LM2-Atrazine served as our mid¬ 
level spatial resolution atrazine model, and the 
application is discussed in this report. 

1.5.4 Resolution for the LMMBP Models 

Model resolution is the spatial and temporal scale of 
predictions, as well as the definitions of model state 
variables. While factors such as data availability, 
model sophistication, and computer resources 
constrain resolution to a degree, different levels of 


model resolution are possible and are, in fact, 
necessary. Three “levels” of spatial resolution, 
indicated by the segmentation grid of the lake 
surface, are illustrated in Figure 1.5.1. Level 1 was 
resolved at the scale of lake basins (characteristic 
length, L = 150 km) with an associated seasonal 
temporal resolution. This was a screening-level 
model resolution used in MICHTOX. Level 2 was 
resolved at a regional scale defined by food webs (L 
= 40 km); temporal resolution was weekly-to-monthly. 
This resolution was roughly comparable to that 
achieved by models developed in the GBMBP. Level 
3 was a hydrodynamic scale resolution (L = 5 km), 
with associated daily temporal resolution. Both near¬ 
shore and offshore regions can be distinguished with 
the Level 3 resolution. Level 3 was scaled to resolve 
to predict hydrodynamic transport. 



(Screening) 

6 surface segments 
9 water segments 


10 surface segments 
41 water segments 


(High resolution 5km X 5km grid) 
2318 surface segments 
44,042 water segments 
19 sigma layers 


Figure 1.5.1. Surface water segmentation for alternative Lake Michigan mass balance model levels. 


58 















































































































































































































































Although the LaMP and the Great Waters Program 
(GWP) objectives are “lake-wide,” both of these 
emphasize biotic impairments occurring primarily in 
localized, near-shore regions. LaMP objectives also 
require that the transport of contaminants from 
tributaries and other near-shore sources to the open- 
lake be resolved. Therefore, the Level 1 model was 
not adequate for the study objectives. Level 2 
resolution was adequate for most modeling 
objectives but not for resolution of significant 
hydrodynamic impact or near-shore influence of 
atrazine from tributaries. Level 3 resolution was 
required for accurate hydrodynamic modeling and 
was desirable for predicting near-shore gradients, 
especially those formed by transients such as 
thermal bars and upwelling; as well as more 
persistent features such as tributary plumes and 
thermal stratification. Level 3 transport resolution 
also has the potential in relating toxics loading from 
the 10 Areas of Concern (AOCs) adjoining Lake 
Michigan which must be addressed by the Remedial 
Action Plan (RAP) process to the LaMP via the 
LMMBP. 

The modeling design for the LMMBP was based on 
the development of a number of models at three 
levels of resolution. For the atrazine contaminant 
transport and fate models, MICHTOX was resolved 
at Level 1; LM2-Atrazine was resolved at Level 2; 


and LM3-Atrazine was resolved at Level 3. The 
Princeton Ocean Model (POM) and atmospheric 
loading models were resolved at Level 3. Results of 
the hydrodynamic model were spatially and 
temporally averaged prior to coupling to the Level 2 
model. The rationale for specifying different 
resolutions was the hydrodynamic models require a 
Level 3 resolution to offer the best capability for 
transport simulation and forecasting. A lower 
resolution was specified for LM2-Atrazine because 
this model had been demonstrated at this resolution. 

1.5.5 Models Developed and Applied 

The transport and fate atrazine models developed, 
refined, and applied by the Large Lakes and Rivers 
Forecasting Research Branch (LLRFRB) included 
MICHTOX, LM2-Atrazine, and LM3-Atrazine (Figure 
1.5.2). Models developed and run elsewhere 
included a hydrodynamics model (POM) (Schwab 
and Beletsky, 1998), an atmospheric loading model 
based on local observations (Green et al., 2000; 
Miller et al., 2001), a tributary loading model (Hall 
and Robertson, 1998), and the Community Multiscale 
Air Quality (CMAQ) model. CMAQ was adapted to 
simulate the regional atmospheric fate and transport 
of atrazine (Cooter et al., 2002; Cooter and Hutzell, 


Hydrodynamic 

and 

load models 



Contaminant 
transport 
and fate 
models 


atmospheric 

loads 


tributary 

loads 



transport/ advective/ 
aggregated/ dispersive 
transport 
and bottom 
shear stress 


to level 2, 




* *—- 


-—ii 

MICHTOX 


LM2-Atrazine 


LM3-Atrazine 

level 1 


level 2 


level 3 

model 


model 


model 


environmental exposure 
concentration 


Figure 1.5.2. Model construct used for the LMMBP to model atrazine. 


59 



























2002). This atmospheric model utilized atrazine 
emissions from agricultural soils provided by the 
Pesticide Emissions Model (PEM) (Scholtz et al., 
1999; and Scholtz et al., 2002). The CMAQ 
predictions of atrazine in air and rainfall compared 
well with some field observations taken along the 
Lake Michigan shoreline in 1995. Although the 
results from the CMAQ were not used directly in the 
any of the LMMBP atrazine models, the model 
demonstrated a potential for this purpose in future 
modeling efforts. Only the models developed, 
refined, and applied at LLRFRB will be discussed in 
detail within this document. 

1.5.5.1 Lake Process Models 

The mass balance models for atrazine in Lake 
Michigan were comprised of linked hydrodynamic 
(POM) with LM2-Atrazine and LM3-Atrazine. The 
hydrodynamic model-predicted water movements 
necessary to describe the three-dimensional 
transport of dissolved constituents in the water 
column, and these transport parameters were 
incorporated into the water quality models. The 
benefit of using hydrodynamic model output in this 
way relieves the modeler from having to use a tracer 
in the water, such as chloride, to calibrate advective 
and dispersive transport functions. More discussion 
can be found on this topic in Part 4 (LM2-Atrazine) 
and Part 5 (LM3-Atrazine). 

MICHTOX was not linked in any way with the POM 
hydrodynamic model. In MICHTOX, circulation is 
specified as advective and dispersive transport 
functions. This approach suffers the disadvantages 
in that calibration of the transport functions requires 
extensive tracer data (chloride), circulation is not 
predicted by meteorologic forcing functions, and the 
model loses resolution because of the difficulty in 
measuring/calibrating fine-scale transport variability. 
In Green Bay, chloride data was used to calibrate the 
transport functions. However, in the main lake, the 
chloride gradients were not evident, and therefore, 
were of no value for the purpose of calibrating the 
transport functions. MICHTOX vertical and horizontal 
exchange coefficients were obtained from previous 
Great Lakes modeling studies. See Section 3.3.2 in 
the MICHTOX chapter for more discussion on this 
topic. 


The models described the contaminant transport and 
fate within the water column, mass transfer between 
media (air and water), and atrazine degradation via 
total kinetic decay processes. Together, these 
models formed an integrated description of atrazine 
chemical cycling in the aquatic ecosystem with which 
to predict the relationship between loadings and 
concentrations of atrazine in the lake. 

1.5.5.2 Hydrodynamics (POM) 

The Princeton Ocean Model (POM) (Blumberg and 
Mellor, 1980, 1987) was used to compute three- 
dimensional current fields in the lake. The POM 
simulated large- and medium-scale (km) circulation 
patterns, vertical stratification, velocity distribution, 
seiche, and surface waves. This model was also 
used to simulate a thermal balance for the lake. The 
POM is a primitive equation, numerical hydrodynamic 
circulation model that predicts three-dimensional 
water column transport in response to wind stress, 
temperature, barometric pressure, and Coriolis force. 
The POM has been demonstrated to accurately 
simulate the predominant physics of large water 
bodies (Blumberg and Mellor, 1983, 1985; Blumberg 
and Goodrich, 1990). This model was used to 
develop year-long simulations on a 5 km horizontal 
grid with 19 sigma-coordinate vertical layers at one- 
hour intervals for Lake Michigan (Schwab and 
Beletsky, 1998). Observed and simulated 
meteorological data were used to define model 
forcing functions. Extensive measurements of 
temperature and current distributions collected in 
Lake Michigan during 1982-1983 were used to 
provide the necessary data for model calibration; 
measurements of water temperature and current 
distributions were used to confirm hydrodynamic 
simulations for 1994-1995. 

1.5.6 Model Quality Assurance 

A Quality Assurance Project Plan (QAPP) was 
prepared and implemented for the atrazine modeling 
(Richardson et al., 2004). The QAPP specified 
procedures for code development; testing; 
modification; documentation; as well as methods and 
measures applied in model calibration, confirmation, 
and uncertainty analysis. 


60 




1.5.7 Model Application and 
Computational Aspects 

1.5.7.1 Annual Simulations 

Annual forecast simulations were run with the LM3- 
Atrazine model. Model input reflected seasonal, 
regional, and lake-wide contaminant loads. Model 
output was analyzed within the high-resolution of 
spatial and temporal gradients of contaminant 
concentrations. 

1.5.7.2 Long-Term Simulations 

MICHTOX and LM2-Atrazine long-term simulations 
were used to forecast the lake-wide impact of various 
management scenarios. Forecasts were performed 
to determine time to near steady-state conditions for 
both continuing and discontinued loads. Forecasts 
were also run to evaluate reductions in exposure 
concentrations resulting from elimination of tributary 
and/or atmospheric loading. 

References 

Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W. 
Shanz. 1988. WASP4, A Hydrodynamic and 
Water Quality Model - Model Theory, User’s 
Manual, and Programmer’s Guide. U.S. 
Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory, Athens, Georgia. 
EPA/600/3-87-039, 297 pp. 

Blumberg, A.F. and D.M. Goodrich. 1990. Modeling 
of Wind-Induced Destratification in Chesapeake 
Bay. Estuaries, 13(3): 1236-1249. 

Blumberg, A.F. and G.L. Mellor. 1980. A Coastal 
Ocean Numerical Model. In: J. Sunderman and 
K.P. Holtz (Eds.), Mathematical Modeling of 
Estuarine Physics, pp. 203-214, Proceedings of 
the International Symposium, Hamburg, 
Germany, August 1978. 

Blumberg, A.F. and G.L. Mellor. 1983. Diagnostic 
and Prognostic Numerical Circulation Studies of 
the South Atlantic Bight. J. Geophys. Res., 
88(C8):4579-4592. 


Blumberg, A.F. and G.L. Mellor. 1985. A Simulation 
of the Circulation in the Gulf of Mexico. Israel J. 
Earth Sci., 34:122-144. 

Blumberg, A.F. and G.L. Mellor. 1987. A Description 
of a Three-Dimensional Coastal Ocean 
Circulation Model. In: N.S. Heaps (Ed.), Three- 
Dimensional Coastal Ocean Models, Coastal and 
Estuarine Sciences, pp. 1-16. American 
Geophysical Union, Washington, D.C. 

Cooter, E.J. and W.T. Hutzell. 2002. A Regional 
Atmospheric Fate and Transport Model for 
Atrazine. 1. Development and Implementation. 
Environ. Sci. Technol., 36(19):4091-4098. 

Cooter, E.J., W.T. Hutzell, W.T. Foreman, and M.S. 
Majewski. 2002. A Regional Atmospheric Fate 
and Transport Model for Atrazine. 2. Evaluation. 
Environ. Sci. Technol., 36(21 ):4593-4599. 

Endicott, D.D., W.L. Richardson, and D.J. Kandt. 
2005. 1992 MICHTOX: A Mass Balance and 
Bioaccumulation Model for Toxic Chemicals in 
Lake Michigan. In: R. Rossmann (Ed.), 
MICHTOX: A Mass Balance and 

Bioaccumulation Model for Toxic Chemicals in 
Lake Michigan, Part 1. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and Environmental 
Effects Research Laboratory, Mid-Continent 
Ecology Division-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. EPA/600/R- 
05/158, 140 pp. 

Green, M.L., J.V. DePinto, C.W. Sweet, and K.C. 
Hornbuckle, 2000. Regional Spatial and 
Temporal Interpolation of Atmospheric PCBs: 
Interpretation of Lake Michigan Mass Balance 
Data. Environ. Sci. Technol., 34(9):1833-1841. 

Hall, D. and D. Robertson. 1998. Estimation of 
Contaminant Loading from Monitored and 
Unmonitored Tributaries to Lake Michigan for the 
USEPA Lake Michigan Mass Balance Study. 
Quality Systems and Implementation Plan. 
Submitted October 23,1998. U.S. Environmental 
Protection Agency, Great Lakes National 
Program Office, Chicago, Illinois. 19 pp. 


61 











Miller, S.M., M.L. Green, J.V. DePinto, and K.C. 
Hornbuckle. 2001. Results from the Lake 
Michigan Mass Balance Study: Concentrations 
and Fluxes of Atmospheric Polychlorinated 
Biphenyls and trans- Nonachlor. Environ. Sci. 
Technol., 35(2):278-285. 

Richardson, W.L., D.D. Endicott, R.G. Kreis, Jr., and 
K.R. Rygwelski (Eds.). 2004. The Lake Michigan 
Mass Balance Project Quality Assurance Plan for 
Mathematical Modeling. Prepared by the 
Modeling Workgroup. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and Environmental 
Effects Research Laboratory, Mid-Continent 
Ecology Division-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. EPA/600/R- 
04/018, 233 pp. 

Rygwelski, K.R., W.L. Richardson, and D.D. Endicott. 
1999. A Screening-Level Model Evaluation of 
Atrazine in the Lake Michigan Basin. J. Great 
Lakes Res., 25(1 ):94-106. ' 

Scholtz, M.T., B.J. Van Heyst, and A. Ivanhoff. 
1999. Documentation for the Gridded Hourly 
Atrazine Emissions Data Set for the Lake 
Michigan Mass Balance Study. U.S. 
Environmental Protection Agency, Office of 
Research and Development, National Exposure 
Research Laboratory, Research Triangle Park, 
North Carolina. EPA/600/R-99/067, 61 pp. 


Scholtz, M.T., E. Voldner, A.C. McMillan, and B.J. 
Van Heyst. 2002. A Pesticide Emission Model 
(PEM) Part 1: Model Development. Atmos. 
Environ., 36(32):5005-5013. 

Schwab, D. and D. Beletsky. 1998. Lake Michigan 
Mass Balance Study: Hydrodynamic Modeling 
Project. National Oceanic and Atmospheric 
Administration, Great Lake Environmental 
Research Laboratory, Ann Arbor, Michigan. 
NOAATechnical Memorandum ERLGLERL-108, 
55 pp. 

Sonzogni, W.C., W. Richardson, P. Rodgers, and 
T.J. Monteith. 1983. Chloride Pollution of the 
Great Lakes. Water Pollut. Contr. Fed. J., 
55(5):513-521. 

Thomann, R.V. and J.P. Connolly. 1984. An Age 
Dependent Model of PCB in a Lake Michigan 
Food Chain. U.S. Environmental Protection 
Agency, Office of Research and Development, 
Environmental Research Laboratory-Duluth, 
Large Lakes Research Station, Grosse lie, 
Michigan. EPA/600/S3-84/026, 3 pp. 

U.S. General Accounting Office. 1993. Report to the 
Chairman, Subcommittee on Oversight of 
Government Management, Committee on 
Governmental Affairs, U.S. Senate: Pesticides - 
Issues Concerning Pesticides Used in the Great 
Lakes Watershed. U.S. General Accounting 
Office, Washington, D.C. GAO/RCED-93-128, 
39 pp. 


62 




PART 2 


LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE 

LOADINGS TO LAKE MICHIGAN 


Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research Branch 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 


Chapter 1. Historical Atrazine Usage in 
the United States 

2.1.1 Background 

The company, J.R. Geigy A.G., of Basel Switzerland 
applied for a patent with the United States Patent. 
Office on January 12, 1955 that described a method 
for making atrazine and listed various mixtures with 
the active ingredient that could be used to kill weeds 
(U.S. Patent Office, 1959). Atrazine was registered 
with the United States Department of Agriculture 
(USDA) in 1958 (U.S. Environmental Protection 
Agency, 2003). On June 23, 1959, the atrazine 
patent (Number 2891855) was issued. By the mid- 
1960s, widespread use of atrazine was observed 
(Duke, Ciba Geigy Patent Office, personal 
communication, 1994; Kells, Michigan State 
University, personal communication, 1994). 

In the Lake Michigan basin, atrazine is primarily used 
on corn crops to control broadleaf and some grassy 
weeds by inhibiting photosynthesis. For corn crops, 


it is usually applied to the fields in the spring, prior to, 
during, or after planting a crop or after emergence of 
the corn seedlings. Atrazine is usually mixed in a 
water solution along with other herbicides. Estimates 
by Nako and Keitt (1994) indicate that atrazine is 
relatively inexpensive compared to other herbicides. 
Cost for chemically treating one acre in 1992 was 
about three dollars (estimate does not include costs 
for fuel and labor). During 1994-1995, atrazine use 
as a percentage of total pesticide use in the basin 
was 13.8% (Brody et al., 1998). During the same 
time frame, corn represented 38.8% of planted 
acreage. For the period 1964 through 1993, atrazine 
was the leading herbicide used in the United States 
(U.S. Department of Agriculture, 1994; Lin et al., 
1995). Atrazine trade names/synonyms include: 
Aatrex, Actinite PK, Akticon, Argezin, Atazinax, 
Atranex, Atrataf, Atred, Candex, Cekuzina-T, 
Chromozin, Crisatrina, Cyazin, Fenamin, Fenatrol, 
Gesaprim, Griffex, Hungazin, Inakor, Pitezin, 
Primatol, Radazin, Strazine, Vectal, Weedex A, 
Wonuk, and Zeapos (U.S. Environmental Protection 
Agency, 2006). 


63 











Application rates of atrazine to farm fields have 
decreased over time. In 1990, a label change 
initiated by the manufacturers of atrazine set the 
maximum recommended application rate for atrazine 
to three pounds active ingredient per acre. Prior to 
this, four pounds active ingredient per acre was 
recommended (Scribner et al., 2000). In 1992, the 
manufacturers again voluntarily reduced the 
maximum recommended application rate of atrazine 
on corn and sorghum to a range of 1.6-2.5 pounds 
active ingredient per acre depending on soil organic 
residue and erosion potential. The 1992 label 
changes also included atrazine mixing/loading and 
application setbacks to protect various water sources 
including wells, streams, lakes, and reservoirs 
(Pearson and Giles, 1993). The lower 1992 
application rate was recommended for fields with less 
than 30% plant residues on the surface. The label 
changes reduced recommended application rates by 
nearly 50%, however, actual application rates used 
by farmers decreased by only about 11% from an 
average 1.1 pounds per acre in 1991 to 0.97-0.98 
pounds per acre in 1994-1995. (U.S. Department of 
Agriculture, 2006). Evidently, farmers were satisfied 
with the results from lower than recommended 
application rates set by the manufacturers of the 
herbicide. The reduced application rates in 1994- 
1995 and 1998 compared to 1989-1990 were 
reflected in reduced concentrations observed in 
several Midwestern streams during post-application 
run-off (Scribner et al., 2000). 

2.1.2 Total Annual Usage Estimates 

Usage of atrazine is predominant in the eastern half 
of the United States (see Figure 2.1.1). As depicted 
in the figure, usage is heavy south of the Lake 
Michigan basin in the states of Illinois and Indiana. 
However, except for the northwestern part of Indiana, 
most of the drainage and associated load from these 
two states discharge into the Mississippi River 
watershed. But, the proximity of these high-use 
areas to Lake Michigan does impact the atmospheric 
loading of atrazine to the southern area of the lake. 

In Table 2.1.1, some statistics are presented on the 
usage of atrazine on corn crops in the United States 
for crop years 1991, 1994, and 1995 (U.S. 
Department of Agriculture, 2006). For all three years, 
atrazine was the most used herbicide on corn crops. 


In the survey year 1994, the ranking of the top 10 
states in order of highest corn acreage to lowest was 
Iowa, Illinois, Nebraska, Minnesota, Indiana, South 
Dakota, Wisconsin, Ohio, Michigan, and Montana. 

Figure 2.1.2 depicts county usage of atrazine during 
the Lake Michigan Mass Balance Project (LMMBP). 
Note the highest use region is in the southwestern 
part of Michigan and northern Indiana. Little atrazine 
is used in the northern parts of the basin. The data 
for 1994 were provided by Kirschner (International 
Joint Commission, personal communication, 1997) 
and the data from 1995 were provided by Macarus 
(U.S. Environmental Protection Agency, personal 
communication, 1999). 

Historical total annual atrazine usage estimates in the 
United States are depicted in Figure 2.1.3 for years 
where data were available. The data used in the 
graphic are presented in Table 2.1.2. The atrazine 
data (zero usage) for 1963 (Duke, Ciba Geigy Patent 
Office, personal communication, 1994) matches 
estimates made by Scribner et al. (2000). Robert 
Torla’s (United States Environmental Protection 
Agency (USEPA), personal communication, 1994) 
data (1964, 1966, and 1971) are from 

USDA/Economic Research Service (ERS) published 
estimates (U.S. Department of Agriculture, 2003), 
and the rest of the data are from Aspelin and Nako 
(USEPA, personal communication, 1997). The data 
represent total annual usage (both agricultural and 
non-agricultural). However in the 1990s, it was 
estimated that approximately 95% was used for 
agricultural purposes. For some of the years (such 
as 1993 and 1995), a range of values was reported. 
When this occurred, a mean of the range was used. 
Also plotted on Figure 2.1.3 are historical (1964- 
2002) total United States acreage for corn and 
another for the sum acreage of corn, sorghum, and 
sugarcane - all crops that use atrazine to suppress 
weeds. Notice that the pattern of atrazine use, 
except for the earliest years, follows the pattern of 
corn acreage planted in the United States. The low 
corn acreage in 1983 and 1988 were due to drought 
conditions (Shapouri et al., 1995). 

2.1.3 Future Atrazine Use Estimates 

Atrazine currently holds its large market share 
because it is a pre-emergent herbicide active against 
most of the serious broadleaf weeds in corn, and it is 


64 




Graphic by William Battaglin 
U.S. Geological Survey 


Kilograms per 
Square Kilometer 

Missing or 0 

Less than 0.5 

0.5 to 2.5 

2.6 to 10.0 

10.1 to 25.0 

more than 25.0 


300 Miles 

L 


n 

500 Kilometers 


Figure 2.1.1. Atrazine usage in the United States for 1991. 


Table 2.1.1. U.S. Department of Agriculture Corn Crop Summaries of Atrazine Usage in the United 
States for 1991, 1994, and 1995 


Year 

Number of 
States 
Surveyed 

% of Total 
Corn Crop 
Surveyed 

% of Corn 
Crop Treated 
With 

Herbicides 

% of Corn 
Crop Treated 
With 
Atrazine 

Average 
Application 
Rate of 
Atrazine 
(Ibs/acre) 

Total Amount 
of Atrazine 
Applied 
(millions of 
kg) 

1991 

17 

90 

94 

66 

1.1 

23.61 

1994 

10 

79 

98 

68 

0.97 

20.59 

1995 

15 

90 

96 

65 

0.98 

20.74 


65 



































































































[ 0-12000 
I" 12001-24000 
a 24001-36000 
iHl 36001-48000 
■148001-60000 
Kilograms of Atrazine Applied 


Figure 2.1.2. Estimates of atrazine usage in the Lake Michigan basin for 1994 and 1995. 



Figure 2.1.3. Historical trend of total annual usage of atrazine in the United States with acreage 
planted in corn, sorghum, and sugarcane. 


66 






















































































Table 2.1.2. Total Annual Usage of Atrazine in the United States (Aspelin and Nako, U.S. Environmental 
Protection Agency, Personal Communication, 1997; Torla, U.S. Environmental Protection Agency, 
Personal Communication, 1994) 


Year 

Millions of kg Atrazine Used in the United States 

1963 

0.0 

1964 

6.3 

1966 

12.0 

1971 

25.8 

1974 

31.8 

1976 

36.3 

1978 

39.9 

1980 

38.6 

1982 

35.4 

1984 

39.9 

1986 

35.8 

1989 

34.9 

1991 

34.0 

1993 

34.7 

1995 

33.8 


inexpensive (Nako and Keitt, 1994). Any 
replacements must be equally as effective in 
controlling weeds and matching or beating costs. 
Due to repeated annual usage, some weeds, such as 
pigweed, are showing resistance to triazine 
herbicides. Blending other herbicides with atrazine 
may help to eliminate some of these resistant plants. 

If the resistant plants do not have an efficient seed 
dispersal mechanism, then these problem plants 
become a local problem. However, if the resistant 
plant shows resistance to other herbicides and has 
an effective seed dispersal mechanism, then usage 
of atrazine may decline. Another factor to consider 
in projecting future usage is possible regulatory 
action that could restrict usage in some manner. 
With growing ethanol demand and strong export 
sales of corn, U.S. farmers planted 92.9 million acres 
of corn in 2007. This exceeded the 2006 acreage by 
19 percent (U.S. Department of Agriculture, 2007). 
The actual planted acreage is the highest since 1944. 


References 

Brody, T.M., B.A. Furio, and D.P. Macarus. 1998. 
Agricultural Pesticide Use in the Great Lakes 
Basin: Estimates of Major Active Ingredients 
Applied During 1994-1995 for the Lake Erie, 
Michigan, and Superior Basins. U.S. 
Environmental Protection Agency, Region 5, 
Chicago, Illinois. 15 pp. 

Lin, B., M. Padgitt, L. Bull, H. Delvo, D. Shank, and 

T. Harold. 1995. Pesticide and Fertilizer Use 
and Trends in the U.S. Agriculture. U.S. 
Department of Agriculture, Economic Research 
Service, Washington, D.C. Document Number 
717, 56 pp. 

Nako, S. and G. Keitt. 1994. Use of Triazines and 
Other Herbicides for Broadleaf Control on Corn. 

U. S. Environmental Protection Agency, Office of 
Pesticide Programs, Biological and Economic 
Analysis Division, Washington, D.C. 7 pp. 




67 











Pearson, D. and E. Giles. 1993. Atrazine Label 
Changes. Resource Update One, Illinois Food 
and Agriculture Council, Urbana, Illinois. 

Scribner, E.A., W.A. Battaglin, D.A. Goolsby, and 
E.M. Thurman. 2000. Changes in Herbicide 
Concentrations in Midwestern Streams in 
Relation to Changes in Use, 1989-1998. Sci. 
Total Environ., 248(2/3):255-263. 

Shapouri, H., J.A. Duffield, and M.S. Graboski. 
1995. Estimating the Net Energy Balance of 
Corn Ethanol. U.S. Department of Agriculture, 
Economic Research Service, Office of Energy 
and New Uses, Washington, D.C. Agricultural 
Economic Report Number 721,24 pp. 

U.S. Department of Agriculture. 1994. Agricultural 
Resources and Environmental Indicators. U.S. 
Department of Agriculture, Economic Research 
Service, National Resources and Environment 
Division, Washington, D.C. 216 pp. 

U.S. Department of Agriculture. 2003. Historical 
Track Records - National Agricultural Statistics 
Service. Available from U.S. Department of 
Agriculture at http://usda.mannlib.Cornell. 
edu/usda/nass/96120/trackrec2003.txt 

U.S. Department of Agriculture. 2006. Agricultural 
Chemical Usage - 1991,1994, 1995 Field Crops 
Summary. National Agricultural Statistics 
Service, Washington, D.C. Available from U.S. 
Department of Agriculture at http://usda/ 
mannlib.cornell.edu/data-sets/inputs/9x171. 


U.S. Department of Agriculture. 2007. National 
Agricultural Statistics Service. U.S. Department 
of Agriculture, Washington, D.C. Available from 
U.S. Department of Agriculture at http://www. 
nass.usda.gov. 

U.S. Environmental Protection Agency. 2003. 
Pesticides: Topical and Chemical Fact Sheets - 
Atrazine Background. U.S. Environmental 
Protection Agency, Office of Pesticide Programs, 
Washington, D.C. Available from U.S. 
Environmental Protection Agency at http://www. 
epa.gov/pesticides/factsheets/atrazine_ 
background. 

U.S. Environmental Protection Agency. '2006. 
Consumer Factsheet on: Atrazine. U.S. 
Environmental Protection Agency, Ground Water 
and Drinking Water, Washington, D.C. Available 
from U.S. Environmental Protection Agency at 
http:/www. epa.gov/safewater/dwh/csoc/atrazine. 

U.S. Patent Office. 1959. Compositions and 
Methods for Influencing the Growth of Plants. 
Assignors: Hans Gysin and Enrico Knusli, J.R. 
Geigy A.G., Basel, Switzerland. Patent Number: 
2891855; Serial Number 481474. 


68 



PART 2 


LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE 

LOADINGS TO LAKE MICHIGAN 


Chapter 2. Estimation of Atrazine 
Tributary Loadings 

Tributary loadings for the Lake Michigan Mass 
Balance Project (LMMBP) atrazine models were 
estimated using an approach based on watershed 
export of the applied chemical from farm fields to the 
lake and another approach that utilized LMMBP 
measurements of atrazine concentration and flow in 
the 11 monitored streams within the construct of the 
Stratified Beale Ratio Estimator (SBRE) to calculate 
loadings. Watersheds that drained into a monitored 
tributary were identified as a monitored watershed. 
The other watersheds in the Lake Michigan basin 
were identified as unmonitored. Both methods made 
estimates of watershed loadings of atrazine to Lake 
Michigan for both the monitored and unmonitored 
watersheds. The MICHTOX and LM2-Atrazine 
models solely utilized estimates based on watershed 
export. The LM3-Atrazine model utilized load 
estimates based on the SBRE and on a hybrid of the 
two load estimates, whereby the SBRE loads were 
enhanced with additional loadings based on the 
annual watershed export estimates (see Part 5, 
Chapter 3, Section 5.3.3.3.1). Both the MICHTOX 
and LM2-Atrazine models utilized annualized 
loadings only and are useful for long-term 
simulations. LM3-Atrazine loadings were calculated 
on a daily basis so as to capture seasonal variations 
on a finer time and spatial resolution that were not 
available in either of the other two models. 

Watershed loading estimates were made for all of the 
Lake Michigan sub-basins. A sub-basin may have an 
identifiable tributary that discharges this loading into 


the lake, or it may not. However they were 
calculated, both were collectively referred to as 
tributary loadings. 

2.2.1 Atrazine Tributary Load Estimates 
Utilizing County-Level Atrazine 
Application Data 

Literature values for estimates of the percentage of 
the amount of atrazine applied in a watershed that is 
delivered to a receiving body of water were used in 
the loading estimates. This percentage is identified 
as the Watershed Export Percentage (WEP), but it is 
also referred to in the literature as Load as a 
Percentage of Use (LAPU). The calculation of 
atrazine tributary loads (mass/time) to a MICHTOX or 
LM2-Atrazine segment for a given year when 
application rates and corn acreage are known were 
calculated as follows: 


Watershed Export Load or Tributary Load = 




x 


TxCxFxL 1 

J 7 


( 2 . 2 . 1 ) 


where 

j ~ a county within a Hydrological Unit Code 
(HUC) draining into a given water segment 

n = total number of counties in a HUC 

k = the load from a given HUC in a sub-basin 
delivered to a model segment 


69 











m = total number of HUC loads in a sub-basin 
delivered to a model segment 

A = atrazine application rate (mass/acre/time) for 
corn 

T = fraction of corn acreage treated with atrazine 
C = corn acreage in a given county 
F = fraction of county within the HUC 
L = (Watershed Export Percentage)/100 

2.2.1.1 County-Level Atrazine Application Data 

In this project, we received atrazine data in the form 
of county-level application estimates for a given year 
or the product of variables A, T, and C in Equation 
2.2.1. Sources of these data are identified in Table 
2.2.1. As is evident from the table, data were only 
available for six years. Additional data beyond 2002 
are likely but were not included in this analysis. The 
atrazine data were reported as an active ingredient, 
so no conversion was required before model loadings 
were estimated. 


The area fraction of a given county that lies within a 
HUC in the Lake Michigan basin was determined by 
Geographical Information System (GlS)-defined HUC 
boundaries and county boundaries. Within the basin, 
there are a number of HUCs that collectively form 
sub-basins. Some of these sub-basins defined 
watersheds of the 11 LMMBP major tributaries in the 
Lake Michigan basin. Other sub-basins were not 
readily identifiable with tributaries; however, load 
estimates, identified as unmonitored tributary loads, 
were made for these sub-basins and the discharge 
into the lake was associated with a model segment. 
The GIS was used to calculate what fraction of a 
county fell into a given HUC. Note that more than 
one county may fall within a given HUC. 

2.2.1.2 The Watershed Export Percentage 

The atrazine WEP (variable L in Equation 2.2.1) is 
known to be a function of soil type, population of 
atrazine-degrading bacteria in the soil, field 
topography, timing and amount of rainfall after 
application, and other explanatory variables. 
Seventy-six reservoir drainage basins in the 
Midwestern United States were studied using multiple 
linear regression and logistic regressions to 


Table 2.2.1. Sources of County-Level Atrazine Application Data for the Lake Michigan Basin 


Application 

Year 

Data Source 

Affiliation 

Date Received 

1989 

W.A. Battaglin and D.A. 
Goolsby (see reference) 

U.S. Geological Survey 

1995 (Publication Date) 

1992 

B. Kirschner and R. Baksh 
(personal communication) 

International Joint Commission 
(IJC), Windsor, Ontario, Canada 

1994 

1993 

R. Baksh (personal 
communication) 

IJC 

1995 

1994 

B. Kirschner (personal 
communication) 

IJC 

1997 

1995 

D. Macarus (personal 
communication) 

USEPA/Region V, Chicago, 
Illinois 

1999 

1998 

D. Macarus (personal 
communication) 

USEPA/Region V 

2000 


70 






determine the significance of the explanatory 
variables in predicting concentrations of atrazine in 
the reservoirs (Battaglin and Goolsby, 1996). Both of 
the statistical tests used in the analysis found soil 
hydrologic group values to be a significant 
explanatory variable. This same conclusion was 
drawn from studies performed by Blanchard and 
Lerch (2000) in northern Missouri. Small hydrologic 
group values (1.75) are associated with well-drained 
soil (sand and gravel), whereas larger values (>3.25) 
are associated with poorly-drained soil (clays, 
wetlands, urban). Soil textures in Michigan, 
Wisconsin, Illinois, Indiana, and Ohio are shown in 
Figure 2.2.1. These data were obtained from the 
State Soil Geographic (STATSGO) database 
provided by the United States Department of 
Agriculture (USDA), Soil Conservation Service 
(SCS). A review of 1992 and 1993 atrazine field 
application data revealed that approximately 80% of 
the total atrazine application in the Lake Michigan 
basin is applied to crops in the sub-basins that drain 
into the southeastern part of the lake. These sub¬ 
basins include the southwestern quarter of the lower 
peninsula of Michigan including a small portion of 
northern Indiana that also resides in the Lake 
Michigan basin. Soils in that part of the basin can be 

I identified as moderate to fine textures and have 
hydrologic group values ranging from 2.51 to 3.25. 

At the start of a rain event, the rate of rainfall may 
equal the rate of infiltration into the soil. However, 
after some time, the infiltration rate will start to 
decrease asymptotically and reach some constant, 
but lower, infiltration rate. Run-off begins at the point 
the rainfall rate exceeds the infiltration rate. 

A literature review of atrazine watershed export 
percentages is summarized in Table 2.2.2. The raw 
data used in this summary are presented in Table 
2.2.3. Watershed export percentages for various 
watersheds are grouped by soil type. The data are 
from northern watershed systems (Ontario, Canada; 
northern Ohio; northern Iowa; and southern 
Minnesota). In addition, the data reflect watershed 
export percentages that were calculated for an entire 
year. Many published studies of WEP fall short of a 
full year of monitoring and this causes the estimate to 
be biased low for the annual estimate. Based on this 
literature review, a watershed export percentage of 
0.6% was selected for the Lake Michigan and Green 
Bay watersheds to represent the predominant 


moderate texture soil hydrologic group in this area. 
Note that WEPs for clay soils (1.4) are much higher 
than for sandy soils (0.2). WEP differences between 
clay and sandy soils will yield large differences on 
loading estimates as Equation 2.2.1 indicates. In a 
rain event, run-off will occur sooner on non-saturated 
clay soils than non-saturated sandy soils, because 
sandy soils have a higher infiltration rate. So, it is 
important to carefully assess this parameter when 
estimating watershed export of atrazine. Climatic 
conditions for the 12 annual studies used in deriving 
this watershed export percentage for loam/fine- 
textured soils included a balance of five wet and five 
dry years. For the other two years, one was 
considered average in precipitation and conditions for 
the other year were not reported. Including both wet 
and dry years should help minimize bias in the 
estimate, since atrazine-associated run-off in drought 
years has been observed to be lower compared to 
wet years (Richards et at. 1996). A plot of WEPs 
versus watershed size indicated that there was no 
relationship, and this was also the conclusion by 
Capel and Larsen (2001). They evaluated data from 
408 observations of WEPs across numerous types of 
soil textures. Their median WEP was calculated to 
be 0.66% for watersheds less than 100,000 ha. 
Although not calculated, a more rigorous derivation 
of the average watershed export percentage could be 
achieved if a detailed accounting of soil types was 
performed for corn croplands within the basin. With 
that detailed soil type information, a weighted- 
average WEP could be calculated for each sub¬ 
basin. 

2.2.1.3 Calculating the Atrazine Tributary Load 

County-level application data for a given year were 
multiplied by the fractional area of the county in a 
HUC (Equation 2.2.1). This load was further divided 
if a monitored river basin occupied a portion of that 
county. In that case, the atrazine load was further 
divided and apportioned by area to a monitored river 
load and the rest to unmonitored tributary load. This 
procedure was repeated for all counties that had 
overlap in any given HUC within the Lake Michigan 
basin. These fractional application loads (monitored 
and unmonitored) were summed separately for each 
HUC. The point of discharge of the monitored 
tributary into the lake was associated with a model 
segment and likewise for unmonitored tributary loads. 
Only those whole counties or fractional counties that 


71 










(from STATSGO database) 

Figure 2.2.1. Soil textures typical for the Lake Michigan basin and part of the Lake Erie basin. 


Table 2.2.2. Atrazine Watershed Export Data Summarized From the Literature. Raw Data Used to 
Create This Table Can be Found in Table 2.2.3 


Soil Type 

Watershed Export 
Percentage 

Standard 

Deviation 

95% Confidence 
Level 

Range 

Number of 
Studies 

Clay 

1.4 

0.61 

0.94-1.85 

0.11-2.5 

9 

Loam/Fine Textured 

0.61 

0.38 

0.37-0.85 

0.21-1.5 

12 


72 






















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73 














lie within the Lake Michigan basin boundaries were 
considered for tributary load estimation. When taking 
fractional areas of a county, we assumed that 
atrazine usage within the county was uniform. 
Tributary loading estimates were made for each of 
the years in Table 2.2.1 using this method. 

2.2.2 Estimating Atrazine Tributary Loads 
for Years When County-Level Atrazine 
Application Data Was Not Available 

For the six years where county-level application data 
were available, tributary loads were estimated using 
the approach identified in the previous section, 2.2.1; 
however, to make estimates for additional years, an 
approach was selected that utilized some of the 
results from Section 2.2.1 and estimates of total 
annual atrazine usage in the United States. The 
approach was to calculate a Tributary Load Ratio 
(TLR) of known application rates for a given year and 
divide this number by the total annual United States 
usage amount of atrazine for that same year. For 
years when application data are missing but total 
annual usage is known, the ratio multiplied by the 
total annual usage yields an estimate of tributary 
load. Seventeen years of total annual United States 
usage data are displayed in Figure 2.2.2. This 
approach was used for both MICHTOX and LM2- 
Atrazine model runs. 

Tributary Load Ratio = (Tributary Load to Model 
Segment)j{Total Annual USA Atrazine Usage ) 

( 2 . 2 . 2 ) 

For any year (y), where only total annual United 
States usage is known, a tributary load was 
calculated utilizing a tributary load ratio: 

Tributary Load = (Tributary Load Ratio) x 
{Total Annual USA Usage for Any Year{y)) 

(2.2.3) 

Due to label changes that lowered application 
amounts and established planting setbacks from 
water bodies in 1990 and 1992, a decision was made 
to use two TLRs in order to address atrazine 
application practices for pre- and post-label changes. 


For the pre-label change period, tributary load 
estimates for years 1964 through 1986 used the TLR 
calculated for 1989. We used 1989 because this was 
the only year during that pre-label change period 
where we had both county-level application data and 
total annual United States usage estimates. An 
average atrazine application rate of 1.54 pounds/acre 
on corn from a 1982 survey of 16 states with more 
than one million acres of corn compares well to an 
average application rate for Michigan and Wisconsin 
of 1.5 pounds/acre for the same year (Gianessi and 
Puffer, 1988). So for at least that year, the atrazine 
usage rate per acre in the Lake Michigan basin 
matches usage rates in the rest of the major United 
States corn-growing regions. For comparison 
purposes, atrazine tributary load estimates to 
MICHTOX segment 1 were made for the year 1984 
using the TLR method based on total annual United 
States usage estimates for 1984 and also by using 
available atrazine use data (Gianessi and Puffer, 
1988) that included application rate data by state, 
total corn crop acreage by state, and fraction of corn 
crop that was treated with atrazine. The TLR method 
yielded a total tributary load estimate of 15.4 kg/day 
of atrazine to segment one. The tributary load 
estimate based on Equation 2.2.1 and data from 
Gianessi and Puffer yielded a result of 17.7 kg/day. 
For this latter estimate, data on the percent of corn 
acreage treated with atrazine was from 1984 
(Michigan), 1982 (Illinois), and early 1980s 
(Wisconsin and Indiana). Also, the application rate 
data are from 1984 for Michigan, Indiana, and 
Wisconsin and from 1982 for Illinois. Data on the 
percent of corn acreage treated with atrazine for 
Wisconsin and Indiana were based on expert opinion 
of the U.S. Department of Agriculture/Economics 
Research Service, rather than survey data. Total 
corn acreage in each county within the sub-basin 
draining into segment 1 was based on actual survey, 
data for 1984 (Kevin Pautler, U.S. Department of 
Agriculture, personal communication, 1997). Given 
the uncertainties of both methods, the two numbers 
are reasonably close. 

For the post-label change period, an average of the 
tributary ratios for 1992 and 1993 was used to 
calculate tributary loadings for 1991. For the rest of 
the post-label change years 1992, 1993, 1994, 1995, 
and 1998, loads were calculated based on county- 
level application data using Equation 2.2.1. In 


74 




year 


Figure 2.2.2. WEP-based total atrazine tributary loading estimates to Lake Michigan. 


comparing tributary loading ratios for pre- and post¬ 
label change years 1989 and 1995, the TLR for 1995 
(MICHTOX segment 1), which carries most of the 
atrazine tributary loading to Lake Michigan, was 26% 
lower than what it was in 1989. A similar trend was 
noted for the other segments. 

Yet, total annual United States usage only dropped 
three percent from 1989 through 1995, and total corn 
crop acreage fell just 1.16 percent (Good and Irwin, 
2007). This indicates that usage in the Lake 
Michigan basin dropped more relative to the rest of 
the United States during that period. The Lake 
Michigan basin has a number of rivers and lakes. 
Perhaps the label changes requiring setbacks from 
these water bodies reduced the corn acreage and 
hence usage dropped. The state of Wisconsin 
mandated atrazine usage changes that went beyond 
those related to the 1990 and 1992 label changes 
and includes establishment of statewide usage rates, 
application timing, record keeping (Wisconsin 
Register, 2004), and prohibited use areas (Wisconsin 
Register, 2005). Efforts to reduce atrazine usage in 
the state have been ongoing since 1991. Other 
possible reasons for the use reduction in the Lake 


Michigan basin include substitution of different 
herbicides, cropland taken out of corn production, 
changes in farming practices, increase of corn 
acreage outside the Lake Michigan basin that 
replaced lost acreage within the basin, etc. 
Regardless of the reason for the pre- and post-label 
changes reflected in the TLRs, use of two ratios 
seems to be warranted. 

2.2.3 Atrazine Tributary Loads for 
MICHTOX and LM2-Atrazine 

Based on methods described above, total tributary 
loadings to Lake Michigan are depicted in Figures 
2.2.3 and 2.2.4. Since the mid-1980s, atrazine 
tributary loadings have been declining in the Lake 
Michigan basin; however, total annual usage in the 
United States has not changed much since 
approximately 1986. Tributary loadings for 
MICHTOX segments are shown in Figure 2.2.3. Note 
that the watershed delivering atrazine to model 
segment 1 delivers the highest load to the lake. See 
Figure 3.1 in Part 3 for a graphic showing MICHTOX 
segments. This watershed drains the southwestern 
part of the state of Michigan and a section of 


75 


















































4500 



MICHTOX Segment 


Northeastern Indiana. For the whole-lake, the total 
annual tributary load estimate is the same for both 
models. However, LM2-Atrazine had 10 receiving 
surface water segments and MICHTOX had seven 
(six primary segments and a small segment 
representing the lower Fox River). Each surface- 
water segment sharing a boundary with a sub-basin 
received that sub-basin load. 

For years where atrazine application data or total 
annual USA usage are unknown, both MICHTOX and 
LM2-Atrazine calculate loads by assuming that the 
loads between the two years bracketing the missed 
annual loads are linear. 

2.2.4 Atrazine Tributary Load Estimates 
for LM3-Atrazine 


Figure 2.2.3. Tributary loadings to Lake Michigan 
MICHTOX model segments. 



unee 


y Pere 
Marquette 


leboygan 


Milwaukee 

87 




St. Joseph 
1559 


623 


calculated 


atrazine 
loads (kg/year) 


monitored 

tributary 


oads: 4305 


unmonitored 
tributary 
loads: 959 


Muskegon 
144 

Grand 
m^/1264 


Kalamazoo 

484 


Grand Calumet 23 


Figure 2.2.4. WEP-based Lake Michigan tributary 
loadings, 1994. 


The LM3-Atrazine model was not used to conduct a 
hindcast simulation. This model used United States 
Geological Survey (USGS) loading estimates that 
were based on actual measurements of river flow 
and atrazine concentration. Because these load 
estimates were low compared to the WEP-based 
load estimates discussed in the previous section, the 
USGS loadings were adjusted upward in the 
spring/early summer period so that the total annual 
load was equal to the WEP-based annual loading. 
See Section 5.3.3.3.1 in the LM3-Atrazine modeling 
chapter for more information on LM3 tributary 
loadings. 

2.2.4.1 Tributary Sampling Program 

As part of the LMMBP, the USGS calculated loads 
for 11 monitored tributaries in the Lake Michigan 
basin (Hall et al., 1998). Based on these load 
calculations and land use information, estimates of 
loadings from unmonitored areas were made. 
Loadings were calculated for atrazine, 
deethylatrazine (DEA), and deisopropylatrazine 
(DIA). Tributary data used in the load estimates were 
gathered from samples collected from April 4, 1995 
through October 30, 1995 (U.S. Geological Survey 
and Eisenreich, 1997). Samples were collected far 
enough upstream to minimize mixing of lake and 
tributary water. The Grand Calumet, Kalamazoo, 
and Pere Marquette Rivers were generally well-mixed 
throughout the sampling period. The Sheboygan, 
Menominee, Manistique, Muskegon, Grand, and St. 


76 




































































Joseph Rivers were generally well-mixed during the 
winter months and stratified with respect to 
temperature and conductance in summer months. 
The Milwaukee River, and to a lesser extent the Fox 
River, were found to be poorly mixed at irregular 
intervals throughout the sampling period. The 
location and identification of the USGS stations 
sampled can be found in Hall et al. (1998). Sampling 
was conducted by the USGS in cooperation with the 
Wisconsin and Michigan Departments of Natural 
Resources, the Wisconsin State Laboratory of 
Hygiene, and the University of Wisconsin Water 
Chemistry Program. The primary objective of the 
contaminant-loading data was to provide a detailed 
space and time tributary loading history for input into 
the LMMBP LM3-Atrazine model. 

To reduce errors associated with the load 
calculations, sampling was deliberately biased toward 
high-flow conditions where more than 20% of 
samples were collected at times of discharge above 
the 20% exceedance, (Dolan et al., 1981; Hall et al., 
1998). The assumption is that during the high-flow 
periods, most of the load is transported. Sampling 
for atrazine was delayed for one year due to 
uncertainty in selection of methods and laboratory. 
As a consequence of having only seven months of 
load data to quantify atrazine loadings, the USGS 
believed that the atrazine load estimates based on 
actual concentration and flow measurements were 
not as good as estimates for the other mass balance 
contaminants of interest that were based on 19 
months of measurements. Furthermore, load error 
estimates for atrazine were especially poor, again 
due to the short sampling period. 

Three to four sampling crews in three states were on 
call to capture storm-induced flow events (Hall, U.S. 
Geological Survey, personal communication, 2001). 
Weather was monitored 24 hours per day. 
Equipment was used to trigger pagers upon the 
onset of rising hydrographs. Sampling occurred 
during rising, peak, and falling hydrographs. Except 
for the shallow Pere Marquette and Kalamazoo 
Rivers, rivers were sampled at 0.2 and 0.8 of the total 
depth. These samples were taken at the midpoints 
of river panels that divided the total river flow into 
three visually estimated equal flow panels that were 
determined during discharge calibration 
measurements (Hall et al., 1998). These six samples 


were composited into one sample. For the Pere 
Marquette and Kalamazoo Rivers, only three 
samples (one in each flow panel) were composited 
(Hall et al., 1998). A total of 405 samples (including 
quality control samples) were collected. 

River discharge was measured either by stage and 
discharge techniques for the Manistique, Pere 
Marquette, and Kalamazoo Rivers in Michigan or 
acoustic velocity meters for the Muskegon, Grand, 
and St. Joseph Rivers in Michigan; Grand Calumet 
River in Indiana; and Milwaukee, Sheboygan, Fox, 
and Menominee Rivers in Wisconsin (Hall et al. 
1998). 

2.2.4.2 Atrazine Load Estimation for Monitored 
Rivers Using the Stratified Beale Ratio Estimator 
(SBRE) Method 

Concentration data are usually limited due to cost 
constraints; however, flow data are usually readily 
available at short-time intervals. Sampling for the 
LMMBP was focused on high-flow, high- 
concentration events. However, if the mean 
concentration from these limited samples were 
multiplied by the total annual discharge, the load 
estimate would be biased high. The reason it would 
be high is that the mean concentration observed 
would be disproportionately distorted by the number 
of high-flow, high-concentration samples. 

The SBRE method is nearly bias-free when the data 
are sufficient to give acceptable precision to the load 
estimate. The SBRE method used by the USGS for 
the LMMBP can be found in Richards (1994). 
Another factor in the selection of the SBRE is that the 
method is robust over a range of data distributions. 
The method has been the method preferred by the 
International Joint Commission (IJC) for a number of 
years. The SBRE was used for the period April 4, 
1995 through October 30, 1995 when atrazine was 
sampled. 

For the unmonitored period, January 1, 1994 through 
April 3, 1995 and October 31, 1995 through 
December 31, 1995, a combination of Beale-derived 
daily loads and regression loads from the monitored 
period were used to adjust regression-produced daily 
loads from the unmonitored period (Hall, 2004). The 
Beale method does not provide an algorithm to 







extend the loadings derived from the monitored 
period to an unmonitored period. An adjustment 
coefficient was computed by dividing the sum of 
Beale-model daily loads from the monitored period by 
the sum of the Estimator Regression Model loads for 
the same period. The adjustment coefficient was 
then multiplied by each daily load produced by the 
selected regression model for each of the two 
unmonitored periods to produce “corrected” daily 
loads. For example, if the Beale model was 
producing a sum of daily loads greater than the sum 
of the regression model daily loads for the monitored 
period, the adjustment coefficient would be greater 
than one and the adjustment multiplication would 
linearly increase each regression-daily load in each 
of the two unmonitored periods. 

The 1995 USGS SBRE tributary loadings are 
depicted in Figure 2.2.5. Median river flows and 
median atrazine concentrations are also shown. The 
rivers are ordered based on the highest load on the 
left to the lowest load on the right. Note that although 
the Grand Calumet had the lowest atrazine load, it 


did have the fourth highest median atrazine 
concentration. 

2.2.4.3 Atrazine Load Estimation for 

Unmonitored Watersheds 

Hall (2004) presents material on the method used to 
estimate daily loading from watersheds in the Lake 
Michigan basin where no samples were taken for the 
analytes of interest. Loading estimates derived from 
the 11 monitored tributaries were used to predict 
loadings from the additional 25 unmonitored 
tributaries larger than 325 km 2 . Unit area yields from 
the monitored basin were calculated as follows: 

Unit Area Yield = I,/A (2.2.4) 

where 

li = load estimate for any given day 
A = area of the watershed for a monitored tributary 


1800 — 90 
1600-27 80 


□ atrazine loading (kg/yr) 



-C 

"O 

X 

O 

CD 

C 

CD CD 

c 

CD 

CD 

■a-sc 

Q_ 

c 

o 

O 

<D 

o 


CD 

CD 

3 

c £ 

CD 

CD 

LL 

N 


O) 


CD 

c 

cr 

CD E 

V) 



CD 

3 

CD 

0- 3 

>* 

r— 



O 

O 


E 

CD 


O' 

O 

E 

U) 


CO 



CD 

CD 


co 

3 

CD 

-Q 

CD 

J3 

CO 

o 

c 

CD 

"c 

CD 

2 

lu 

o 


1995 monitored tributaries 


Figure 2.2.5. 1995 USGS SBRE atrazine loadings and median concentrations relative to median flow 
in Lake Michigan tributaries. 


78 




































The USGS used Unit Area Yields from monitored 
watersheds that best matched unmonitored 
watersheds in terms of land use and nature of 
surficial land deposits. A GIS was used to help in the 
watershed classification. Once this classification was 
done, the areas of the 25 unmonitored watersheds 
were expanded to encompass smaller adjacent 
basins that were poorly defined in terms of land use, 
discharge location, and other properties. The sum of 
all monitored and unmonitored watershed loads were 
designed to represent the total loading to Lake 
Michigan from the entire Lake Michigan watershed. 

2.2.5 Comments on Atrazine Tributary 
Loading Estimates 

Estimates of atrazine tributary loadings to Lake 
Michigan for years 1994 and 1995 were made 
independent of the USGS estimates. These 
independent estimates were based on actual 
application of atrazine to the basin and using a 
literature-derived WEP of 0.6%. The following are 
the results: 

1994 USGS: 1163 kg 

1995 USGS: 1426 kg 

1994 WEP-Based: 5263 kg 

1995 WEP-Based: 4916 kg 

The ratio of WEP-based to USGS load for 1994 is 

4.5, and the ratio for 1995 is 3.4. 

For a discussion on possible reasons for the 
discrepancy between the two load estimation 
techniques, see Section 5.3.3.3.1 in this report. 

References 

Battaglin, W.A. and D.A. Goolsby. 1995. Spatial 
Data in Geographic Information System Format 
on Agricultural Chemical Use, Land Use, and 
Cropping Practices in the United States. U.S. 
Geological Survey, Atlanta, Georgia. Water 
Resources Investigations Report 94-4176, 87 pp. 
Available from U.S. Geological Survey at 
http://pubs.usgs.gOv/wri/wri944176/#HDRZ. 


Battaglin, W.A. and D.A. Goolsby. 1996. Using GIS 
and Regression to Estimate Annual Herbicide 
Concentrations in Outflow From Reservoirs in the 
Midwestern USA, 1992-93. In: Proceedings of 
the American Water Resource Association 
Annual Symposium on GIS and Water 
Resources, pp. 89-98. American Water 
Resources Association, Middleburg, Virginia. 

Blanchard, P.E. and R.N. Lerch. 2000. Watershed 
Vulnerability to Losses of Agricultural Chemicals: 
Interactions of Chemistry, Hydrology, and Land- 
Use. Environ. Sci. Technol., 34(16):3315-3322. 

Capel, P.D. and S.J. Larson. 2001. Effect of Scale 
on the Behavior of Atrazine in Surface Waters. 
Environ. Sci. Technol., 35(4):648:657. 

Dolan, D.M., A.K. Yui, and R.D. Geist. 1981. 
Evaluation of River Load Estimation Methods for 
Total Phosphorus. J. Great Lakes Res., 7(3): 
207-214. 

Frank, R. and G.J. Sirons. 1979. Atrazine: Its Use 
in Corn Production and Its Loss to Stream 
Waters in Southern Ontario, 1975-1977. Sci. 
Total Environ., 12(3):223-239. 

Frank, R. and L. Logan. 1988. Pesticide and 
Industrial Chemical Residues at the Mouth of the 
Grand, Saugeen and Thames Rivers, Ontario, 
Canada, 1981-85. Arch. Environ. Contam. 
Toxicol., 17(6):741 -754. 

Gianessi, L. P. and C.M. Puffer. 1988. Use of 
Selected Pesticides for Agricultural Crop 
Production in the United States, 1982-1985. U.S. 
Department of Commerce, National Technical 
Information Service, Springfield, Virginia. 
Document Number PB89-191100, 490 pp. 

Good, D. and S. Irwin. 2007. Marketing and Outlook 
Briefs-2007 U.S. Corn Production Risks: What 
Does History Teach Us? U.S. Department of 
Agricultural and Consumer Economics, University 
of Illinois at Urbana Champaign. May 2007 
Issue/MOBROI -07. 


79 








Hall. D.W., T.E. Behrendt, and P.E. Hughes. 1998. 
Temperature, pH, Conductance, and Dissolved 
Oxygen in Cross Sections of. 11 Lake Michigan 
Tributaries, 1994-95. U.S. Geological Survey, 
Middleton, Wisconsin. Open File Report 98-567, 
85 pp. 

Hall, D.W. 2004. Quality Systems and 
Implementation Plan (QSIP) in the Quality 
Assurance Project Plan forthe LMMBP Modeling. 
In: W.L. Richardson, D.D. Endicott, R.G. Kreis, 
Jr., and K.R. Rygwelski (Eds.), The Lake 
Michigan Mass Balance Project Quality 
Assurance Plan for Mathematical Modeling, 
Appendix G, pp. 233. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and Environmental 
Effects Research Laboratory, Mid-Continent 
Ecology Division-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. EPA/600/R- 
04/018, 233 pp. 

Richards, R.P. 1994. Tributary Loading Estimates 
for Selected Herbicides in Lake Erie Tributaries of 
Michigan and Ohio. U.S. Environmental 
Protection Agency, Great Lakes National 
Program Office, Chicago, Illinois. 

Richards, R.P., D.B. Baker, J.W. Kramer, and D.E. 
Ewing. 1996. Annual Loads of Herbicides in 
Lake-Erie Tributaries of Michigan and Ohio. J. 
Great Lakes Res., 22(2):414-428. 


Schottler, S.P., S.J. Eisenreich, and P.D. Capel. 
1994. Atrazine, Alachlor, and Cyanazine in a 
Large Agricultural River System. Environ. Sci. 
Technol, 28(6):1079-1089. 

Squillace, P.J. and E.M. Thurman. 1992. Herbicide 
Transport in Rivers: Importance of Hydrology 
and Geochemistry in Nonpoint Source 
Contamination. Environ. Sci. Technol., 
26(3):538-545. 

U.S. Geological Survey and S. Eisenreich. 1997. 
USGS Field Operation Plan: Tributary 

Monitoring, Version 1. In: L. Blume (Ed.), Lake 
Michigan Mass Balance Study (LMMB) Methods 
Compendium, Volume 1: Sample Collection 
Techniques, pp. 215-219. U.S. Environmental 
Protection Agency, Great Lakes National 
Program Office, Chicago, Illinois. EPA/905/R- 
97/012a, 403 pp. 

Wisconsin Register. 2004. Pesticide Product 
Restrictions. State of Wisconsin, Madison, 
Wisconsin. Document Number 586:1244-147. 

Wisconsin Register. 2005. Atrazine Prohibition 
Areas, Appendix A. State of Wisconsin, Madison, 
Wisconsin. Document Number 591:149-251. 


80 



PART 2 


LAKE MICHIGAN MASS BALANCE PROJECT ATRAZINE 

LOADINGS TO LAKE MICHIGAN 


Chapter 3. Estimation of Atrazine Loads 
in Wet Deposition (Precipitation) 

2.3.1 Atmospheric Components 
Considered in Modeling Atrazine in Lake 
Michigan 

Both the MICHTOX and LM2-Atrazine models utilize 
annualized wet deposition loadings for long-term 
simulations. However, LM3-Atrazine wet deposition 
loadings were calculated on a daily basis to capture 
seasonal loading variations. LM3-Atrazine was used 
to make predictions in lake segments on short-time 
scales in a fine-grid framework as a function of 
seasonally varying loads - both wet deposition and 
tributary. 

Particulate deposition was not considered in the 
MICHTOX, LM2-Atrazine, and LM3-Atrazine models 
because studies have shown that atrazine deposition 
associated with atmospheric particulates represents 
a minor fraction of the total deposition of atrazine 
(Nations and Hallberg 1992; Siebers et al. 1994). In 
the Lake Michigan Mass Balance Project (LMMBP) 
(Section 1.3.2.2.2), the detection limit for atrazine 
associated with atmospheric particulates was 
relatively high. As a consequence, there was a low 
number of detects at land-based collection sites 
positioned around the lake. Attempts to measure 

atrazine-associated particulates over-the-lake yielded 

only two detects, and both of them were in the 
southernmost part of the lake near major atrazine 
i sources. To make an estimate of atrazine deposition 
fluxes associated with particulates, one needs both 


reliable measurements of atrazine concentration on 
the particles and an estimate of the deposition rate of 
the particles. To calculate a rate of deposition, 
particle sizes are needed. Particle size fractionation 
was not part of the LMMBP analysis. 

Some researchers have attempted to make 
estimates of atmospheric, particulate-associated 
atrazine fluxes to Lake Michigan using some 
assumptions about the particle sizes. Miller et al. 
(2000) roughly estimated that the load from particles 
for the high-loading spring months (April through 
June, 1994-1995), could range from 230 to 1000 
kg/yr. Schottler and Eisenreich (1997) estimated that 
the atrazine-associated particulate load to the lake 
for the period 1991 to 1994 was approximately 160 
kg/yr. Sweet and Harlin (1998) estimated that the 
1994-1995 atrazine-associated particulate load to the 
lake using data from April through July to be about 
220 kg/yr. Using these estimates, as well as wet 
deposition and tributary loadings for 1994, the 
relative contribution of dry particulate deposition to 
the total load of atrazine to the lake 
(wet+dry+tributary) is 2.8% to 11.4% (Miller et al., 
2000), 2.0% (Schottler and Eisenreich, 1997), and 
2.7% for Sweet and Harlin (1998). Note that these 
estimates were based on particulates collected at 
land-based stations around the lake. However, other 
than the two atrazine-associated particulate detects 
in the southernmost part of the lake, we have no 
evidence that these loadings are occurring over-the- 
lake. 

Vapor phase concentrations of atrazine were used in 
the models as a boundary condition; please see 


81 






Parts 4 (LM2-Atrazine) or 5 (LM3-Atrazine) for 
details. 

2.3.2 Atrazine Wet Deposition Load 
Estimates Based on Measured Fluxes in 
the Basin 


region. This is the same percentage used to 
estimate the atrazine tributary load export from the 
Lake Michigan watershed. Higher fluxes of atrazine 
to Lake Michigan are noted in the southern part of 
the lake compared to the northern part. This gradient 
is the result of higher use of the chemical in the 
states south and west of the lake and wind patterns. 


Over-the-lake wet deposition of atrazine for 1991 
(Figure 2.3.1) was based on data collected from 
shore-based samples (Goolsby et al. 1993). 
Goolsby’s study area included Midwestern and 
Northeastern states in a geographic rectangle 
defined by the states North Dakota, Kansas, Virginia, 
and Maine. It is interesting to note that the total 
amount of wet-deposited atrazine in this region is 
calculated to be 0.6% of the amount applied in the 



Estimated atrazine 
deposition in micrograms 
per square meter 
per year -1991 


0 200 Miles 


0 300 Kilometers 



Less than 10 
10 to 25 
26 to 50 
51 to 100 


more than 100 


Figure 2.3.1. Wet deposition (rain and snow) of 
atrazine for 1991 for Midwestern United States 
(Figure by W.A. Battaglin, U.S. Geological Survey, 
1997). 


Wet deposition data for 1994 and 1995 associated 
with the LMMBP were received from Hornbuckle 
(University of Iowa, personal communication, 1999). 
These over-the-lake wet deposition estimates were 
used in all three models. Figure 2.3.2 depicts wet 
deposition for the month of May 1994, and again the 
southern region depicts higher atrazine fluxes. There 
is a strong seasonal trend of wet deposition loadings 
to the lake (Figure 2.3.3) - high loadings in the spring 
and early summer and very little loading during the 
rest of the year. Translating Hornbuckle’s loadings 
into wet deposition fluxes over Lake Michigan and 
Green Bay yielded a value of 30.8 pg/m 2 /yr for 1994 
and 1995. A similar calculation of flux for 1991 
(Figure 2.3.1) yielded a value of 45 pg/m 2 /yr. 

Wet deposition to the lake other than 1991, 1994, 
and 1995 was estimated from total annual usage 
estimates in a similar manner as described for 
historical tributary loadings. However, instead of a 
“Tributary Load Ratio,” a “Precipitation Load Ratio” 
was defined. Precipitation ratios were calculated as 
an average for years 1991, 1994, and 1995 as 
follows: 

Precipitation Load Ratio = (Precipitation 
Load to a Model Segment)/(Total Annual 
USA Atrazine Usage) (2.3.1) 


For any year (y), where only total annual United 
States usage is known, a segment load was 
calculated utilizing the precipitation ratio: 

Precipitation Load = 

(Precipitation Load Ratio) x 

(Total Annual USA Usage Year (y)j (2.3.2) 

Along with total annual usage estimates, annual 
atrazine wet deposition and tributary loadings for 
Lake Michigan and Green Bay are depicted in Figure 


82 



















































































Figure 2.3.2. Gradients of atrazine in wet 
deposition loadings over Lake Michigan for May 
1994. 



Figure 2.3.3. Seasonality of atrazine wet 
deposition loadings to Lake Michigan for 1994- 
1995. 


2.3.4. The wet deposition load calculated for 1995 
was very low compared to 1994 (Figures 2.3.3 and 
2.3.4). It is believed that a cold and wet spring in the 
major corn-growing regions of the United States may 
explain this low estimate (see Section 1.3.2.2.3). 


</> 

E 

CD 

t_ 

05 

o 


0) 

05 

to 

</) 

3 

"to 

3 

C 

C 

to 

< 

C/) 

=> 


45 

40 

35-| 

30 

25 

20 

15 

10 - 

5 


Q total annual usage 
■ historical tributary load 
□ wet deposition 


l L- 


141 


> 

05 

9000 35 


■6000 % 
■5000 | 
•4000 ~ 

CD 

■3000 5 

“O 

■2000 re 
■1000 


0 

1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 

year 


0 


Figure 2.3.4. Total atrazine tributary loading and 
wet deposition loading estimates to Lake 
Michigan. 


2.3.3 Atrazine Wet Deposition and 
Tributary Loads for MICHTOX and LM2- 
Atrazine 

Both tributary and precipitation loadings for the 
MICHTOX and LM2-Atrazine models’ surface water 
segments are shown in Figures 2.3.5 and 2.3.6, 
respectively. In MICHTOX, the southern third of the 
lake is identified as segment 1, the central lake 
region is segment 2, and the northernmost part of the 
lake is segment 3 (see Part 3, Figure 3.1). Note that 
total loadings are greater in the southern region of 
the lake compared to the northern region. In LM2- 
Atrazine, the southern third of the lake is represented 
by segments 1 and 2; central lake, 3 and 4; and the 
northern lake, 5 and 6. Segments 2, 4, and 6 are 
located on the eastern side of the lake. The rest of 
the segments are located in Green Bay. See Figure 
4.1 in Part 4 for a graphic identifying segments for 
LM2-Atrazine. The highest load to LM2-Atrazine is in 
segment 2. Both MICHTOX and LM2-Atrazine 
perform a linear interpolation to estimate missing 
loads between dates that have known loads. For the 
whole lake, the total annual load estimates were the 


83 

















































































4500 


cn 


a> 

c 

N 

ro 


4000 

3500 

3000 

2500 

2000 

1500 

1000 

500 

0 




[01994 tributary loads 



□ 1995 tributary loads 

■ 1994 wet deposition 

□ 1995 wet deposition 






1 2 - 3 4 5 6 7 


MICHTOX Segment 

Figure 2.3.5. Tributary and wet deposition loadings to MICHTOX model segments for 1994 and 1995. 



Figure 2.3.6. Tributary and wet deposition loadings to LM2-Atrazine model segments for 1994 and 
1995. 


84 



















































































same for MICHTOX and LM2-Atrazine; however, 
MICHTOX had seven receiving surface water 
segments and LM2-Atrazine had 10. See Part 5 for 
information on tributary loads and wet deposition 
estimates used in LM3-Atrazine. 

References 

Goolsby, D.A., E.M. Thurman, M.L. Pomes, M. 
Meyer, and W.A. Battaglin. 1993. Occurrence, 
Deposition, and Long Range Transport of 
Herbicides in Precipitation in the Midwestern and 
Northeastern United States. In: D.A. Goolsby, 
L.L. Boyer, and G.E. Mallard (Eds.), Selected 
Papers on Agricultural Chemicals in the Water 
Resources of the Midcontinental United States, 
pp. 75-89. U.S. Geological Survey, Denver, 
Colorado. Document Number 93-418, 89 pp. 

Miller, S.M., C.W. Sweet, J.V. DePinto, and K,C. 
Hornbuckle. 2000. Atrazine and Nutrients in 
Precipitation: Results From the Lake Michigan 
Mass Balance Study. Environ. Sci. Technol., 
34(1 ):55-61. 


Nations, B.K. and G.R. Hallberg. 1992. Pesticides 
in Iowa Precipitation. J. Environ. Dual., 
21 (3):486-492. 

Siebers, J., D. Gottschild, and H.G. Nolting. 1994. 
Pesticides in Precipitation in Northern Germany. 
Chemosphere, 28(8):1559-1570. 

Schottler, S.P. and S.J. Eisenreich. 1997. Mass 
Balance Model to Ouantify Atrazine Sources, 
Transformation Rates, and Trends in the Great 
Lakes. Environ. Sci. Technol., 31 (9):2616-2625. 

Sweet, C.W. and K.S. Harlin. 1998. Atmospheric 
Deposition of Atrazine to Lake Michigan. 
Presented at the Air and Waste Management 
Association’s 91st Annual Meeting and 
Exhibition, June 14-18, 1998, San Diego, 
California. Illinois State Water Survey, 
Champaign, Illinois. Report Number 98-TA37.02. 






PART 3 


LAKE MICHIGAN MASS BALANCE PROJECT 
LEVEL 1 MODEL: MICHTOX-ATRAZINE 


Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research Branch 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 


3.1 MICHTOX-Atrazine Executive 
Summary 

Ourcoarse-segmented model, MICHTOX, was run in 
a hindcast and forecast mode under various load 
modification scenarios. A calibration run based on 
average boundary conditions using historical loadings 
of atrazine to Lake Michigan suggests that 
approximately 1% of the atrazine in the lake decays 
each year. In the forecasts of alternate futures, 
eliminating all loadings to the lake resulted in the 
largest decline in model predictions. A total loading 
reduction of approximately 37%, if implemented on 
January 1, 2005, would have been needed in order 
to prevent atrazine concentrations from increasing 
further than above those that were observed in the 
lake on January 1,2005. 

3.2 MICHTOX-Atrazine Recommendations 

For long-term forecasts, future modeling efforts 
should utilize LM2-Atrazine as a model because it is 
more highly resolved and has advective and 
dispersive components that were derived from a 


hydrodynamic model (see Part 4). The 
hydrodynamic model components can be considered 
to best represent “average” lake conditions because 
the various hydrodynamic forcing functions were 
considered to be average (see Part 1, Chapter 4). 

3.3 Model Description 
3.3.1 Model Overview 

For calibration purposes, the MICHTOX mass 
balance model (Endicott et al., 2005) was used in a 
hindcast mode to simulate atrazine concentrations in 
Lake Michigan and Green Bay in response to mass 
loadings to these systems from the time of 
introduction in the early 1960s to 1995. The 
calibrated model was then used in a forecast mode 
to predict lake-wide concentrations in Lake Michigan 
as a function of various loading scenarios. 

MICHTOX was adapted from the general water 
quality model WASP4 (Ambrose et al., 1988). The 
model solves mass balance equations based on a 
finite volume spatial discretization (Thomann and 


86 



Mueller, 1987) and Euler time integration. The 
MICHTOX model framework is capable of 
incorporating a full range of transport and fate 
processes such as advection, dispersion, particle 
settling, sediment resuspension, sediment burial, 
transport in sediment pore water, partitioning to 
particles, chemical reaction, volatilization, and 
absorption. 

3.3.2 MICHTOX Model Segmentation and 
Circulation 

I The segmentation schematic for Lake Michigan and 
Green Bay is depicted in Figure 3.1. Lake Michigan 
and Green Bay have nine water segments. Surface 
segments 1 (southern lake), 2 (central lake), and 3 
(northern lake) cover the entire main lake. Segments 
5 (southern bay), 6 (central bay), and 7 (northern 
bay) cover Green Bay. Hypolimnetic water segments 
in the main lake are numbered 8 (southern lake), 9 
(central lake), and 10 (northern lake). Segment 4 is 
a very small segment located in the lower Fox River. 
During a period of approximately 100 days in the 
summer, flow and exchange across the Straits of 
Mackinac occurs in two discrete layers between the 
surface water (segment 3) and Lake Huron and 
between the deep hypolimnetic water (segment 10) 
and water that primarily originates from Lake 
Superior (Quinn, 1977) mixed with water from Lake 
Huron. During this period of stratification, surface 
layer flow (segment 3) is from Lake Michigan to Lake 
Huron, and a deeper return flow to Lake Michigan is 
observed. It has been observed that Lake Superior 
water discharging from the St. Marys River travels in 
a persistent westerly direction during stratification 
and constitutes a significant component of the return 
flow to Lake Michigan (Ayers et at, 1956; Schelske 
et al. 1976; Saylor, J., National Oceanographic and 
Atmospheric Administration, personal 
communication, 1998). During the unstratified 
period, all of the flow is from Lake Michigan to Lake 
Huron. 

Two-layered flow has been observed at the mouth of 
Green Bay during thermal stratification (Martin et al., 

1995); however, this structured flow process was not 
incorporated in the MICHTOX model framework. 
MICHTOX incorporates the flows between Green 
Bay and Lake Michigan as net flows. 



Figure 3.1. MICHTOX model segmentation. 


The MICHTOX model has two water column layers 
for Lake Michigan to simulate the effects of summer 
stratification of the lake. Also, the model consists of 
just three horizontal compartments in the main lake. 
This low spatial resolution was considered adequate 
to address open-lake concentrations. Water column 
concentration profiles of atrazine at 10 stations, 
representing four to 10 depths per station, showed no 
vertical gradients during lake stratification for the 
years 1991 and 1992 (Schottler and Eisenreich, 
1997) and 1994-1995 (Brent et al., 2001). 
Furthermore, they reported that analysis of data from 
their 10 lake stations that covered a central north- 
south axis and an east-west axis showed no 
horizontal gradients in atrazine concentrations in the 
lake. 

















MICHTOX exchange coefficients were taken from the 
literature. Vertical exchange coefficients, which 
quantify the extent of mixing between epilimnetic and 
hypolimnetic segments in the main lake, were taken 
from the Lake Michigan WASP eutrophication model, 
MICH1 (Rodgers and Salisbury, 1981). Horizontal 
exchange coefficients in Green Bay were calibrated 
to reproduce chloride gradients. In the main lake, 
however, horizontal exchange coefficients were 
taken from work by Thomann et al. (1979) on Lake 
Ontario. 

Flows in the lake were based on the whole-lake water 
balance by Quinn (1977), which provided monthly 
average changes in storage, tributary flow, outflow, 
diversion, precipitation, and evaporation. The 
hydraulic residence time (volume/outflow) for the 
main lake was estimated to be 62 years (Quinn, 
1992). 

3.4 MICHTOX Model Application to Lake 
Michigan 

3.4.1 Screening Model Application 

A screening-level model of MICHTOX was applied 
before Lake Michigan Mass Balance Project 
(LMMBP) loadings were available (Rygwelski et al., 
1999). This early MICHTOX application assumed 
that volatilization was negligible due to a very small 
Henry’s law constant of 8.1 x IQ' 8 (U.S. Department 
of Agriculture, 2001) and that the chemical could be 
modeled as a conservative substance. 

For this screening model, tributary loads were 
estimated based on atrazine applications to the basin 
in 1992 and 1993 using algorithms identified in 
Equation 2.2.1. The watershed export percentage 
(WEP) used was 0.6% (see Table 2.2.2). In order to 
predict loadings for years when application data were 
not available, the loads estimated for 1992 and 1993 
were divided by estimates of total annual United 
States usage of atrazine using Equation 2.2.2 (no 
annual United States usage estimate was available 
for 1992, so an estimate for that year was calculated 
as a mean of United States usage reported for 1991 
and 1993). A mean of these two ratios was assumed 
to be constant over the entire historical record of 
atrazine usage in the basin. For years where only 
total annual usage was available, an estimate of 


loadings could be determined by multiplying the 
mean load ratio by total annual usage. 

Loadings of wet deposition to the lake were obtained 
for 1991 (Goolsby et al., 1993). These wet 
deposition loads were based on actual 
measurements of atrazine in rain and snow. In a 
similar manner as was calculated for tributary loads, 
the load from Goolsby was divided by a mean of the 
total annual United States usage of atrazine for the 
years 1992 and 1993. Usage in the United States 
between 1989 through 1995 was relatively constant 
so errors in substituting a mean of 1992 and 1993 
usage for 1991 were believed to be small. In a 
manner similar to the mean Tributary Load Ratio, a 
mean atmospheric load ratio was used to estimate 
historical wet deposition to the lake. See Figure 3.2 
for both tributary and precipitation atrazine loads. 



Figure 3.2. Total annual estimated tributary and 
precipitation loadings of atrazine to Lake 
Michigan. 


Using the load history and assuming that atrazine 
decay is zero with negligible volatilization, a model 
hindcast run starting in 1964 yielded a good fit with 
lake data (see Figure 3.3). The results shown in the 
figure are from the main lake only and does not 
include Green Bay. The initial conditions in the lake 
model were set to an atrazine concentration of zero. 
No calibration of the model was needed. Also 
depicted are the effects of using the upper and lower 
95% confidence intervals on the 0.6% WEP reported 
in the literature for moderate textured soils (see Part 
2, Chapter 2). As a sensitivity test, a hypothetical 
0.05 per year overall decay constant was 
incorporated into the model. The model is very 


88 




cn 

c 

a 

o 

2 

c 

<n 

o 

c 

o 

u 

to 

-XL 

ro 


- Mean watershed export percentage 0 60 

decay constant 0.0/year 

- Upper 95% confidence interval 

watershed export percentage 0.84 
decay constant 0.0/year 

“ “ Lower 95% confidence interval 
watershed export percentage 0.36 
decay constant 0.0/year 

- Mean watershed export percentage 0.60 

decay constant 0.05/year 

Field Data ±1 standard deviation 


70 -j— 

60 • 

50- 
40- 
30- 
20 - 
10 - 
0- 

1964 1968 



1972 1976 1980 1984 

year 


1988 1992 1996 


water bodies in 1990 and 1992, a decision was made 
to use two Tributary Load Ratios in order to address 
atrazine application practices for pre- and post-label 
changes. New data from the LMMBP also became 
available to modelers. With these additional data, 
loading ratios for both the tributaries and wet 
deposition were updated (see Part 2, Chapters 2 and 
3). The model was calibrated by determining a total 
decay that would yield a best fit of the model to 
observations in the lake. Also, several forecasting 
scenarios were run with the model. The efforts of 
this additional modeling aredescribed inthefollowing 
sections of this part. 

3.4.2.1 Field Data 

See Part 1, Chapter 3 for atrazine data obtained from 
lake, tributaries, and atmospheric components 
samples. 


Figure 3.3. A comparison of MICHTOX - 
Predicted atrazine concentrations in Lake 
Michigan to averaged Lake Michigan data for the 
years 1991, 1992, and 1995 are depicted. Field 
data for 1991 and 1992 were obtained from the 
literature (Schottler and Eisenreich, 1997) and 
data for 1995 are LMMBP data. 


3.4.2.2 Model Assumptions and Calibration 
Procedures 

Due to atrazine’s physical and chemical properties 
(Part 1, Chapters 1 and 3), processes modeled 
included only advection, dispersion, and reaction 
(decay). 


sensitive to this decay as shown in Figure 3.3. 
“Decay” as used in this paper is internal decay likely 
due to the combined effects of abiotic and biotic 
transformation of atrazine to degradation products. 
Considering that the model required no calibration 
and relied mostly on data from the literature, it 
performed remarkably well. 

3.4.2 Enhanced Screening Model 
Application 

As additional county-level atrazine application data in 
the basin and total United States usage estimates 
became available, MICHTOX modeling in Lake 
Michigan continued to develop. In the earlier 
screening model application, only two years of 
county-level atrazine application data were available. 
For the enhanced screening-level model, seven 
years of application data were available and used. 
Also, due to label changes that lowered application 
amounts and established planting setbacks from 


Model processes involving sediments and 
particulates in the water column were not included in 
the MICHTOX model runs because atrazine is 
primarily in the dissolved state in surface waters; 
therefore, any processes that involve sediment or 
suspended particle interactions are of minor 
significance (Section 1.2.2). 

A literature review of atrazine degradation processes 
in surface freshwater presented in Part 1, Chapter 2 
suggests that degradation is hindered in freshwater 
such as in Lake Michigan where the water is cold, 
has low solids concentrations with low dissolved 
organic carbon, has a high pH, and has low 
concentrations of nitrate ions. Degradation of 
atrazine is known to occur through either biotic or 
abiotic processes in some environmental 
compartments. Given the lack of any Lake Michigan- 
specific kinetic information on any of these 
processes, the approach taken in MICHTOX was to 
estimate the loading history of atrazine to the lake 
and find an overall first-order loss rate constant to fit 
the model to observations of atrazine in the lake 


89 














water. Loadings were not part of the calibration a most likely or average boundary condition scenario, 
procedure. Considerable effort was expended to respectively. All of these model runs started on 
ensure that loadings were fairly represented in the January 1, 1963. The model was calibrated by 
model (Part 2, Chapters 2 and 3). finding an appropriate internal decay until the model 

output best matched the observed atrazine 
Due to a very small Henry’s law constant, concentration in the lake for samples taken in 1991, 
volatilization and absorption were not simulated. 1992, 1994, and 1995. 


3.4.2.3 Tributary Loadings 

It was assumed that a WEP of 0.6% derived from the 
literature for fine/moderate textured soils adequately 
described the overall WEP of the Lake Michigan 
watershed. This WEP, along with historical annual 
atrazine usage in the United States was used to 
calculate atrazine loadings from the tributaries to the 
lake. For a complete discussion on the WEP method 
used to estimate MICHTOX loadings, please see 
Part 2, Chapter 2. 

Utilizing flow and concentration data, the Stratified 
Beale Ratio Estimator (SBRE) method was used to 
estimate tributary loads in the 11 monitored 
tributaries during the LMMBP. Also, estimates of 
loads from the unmonitored watersheds were made. 
However, loads were apparently missed and 
therefore MICHTOX tributary loads were only based 
on WEP, county-level application data, and total 
United States annual usage records. See Section 
5.3.3.3.1 for a discussion of this topic. 

3.4.2.4 Atmospheric Loadings 

Loading estimates of wet deposition to Lake 
Michigan and Green Bay were made for each of the 
top surface water segments. These loadings were 
estimated for MICHTOX per discussion in Part 2, 
Chapter 3. 

3.4.2.5 Model Confirmation 

In 2005, atrazine water samples were collected in 
Lake Michigan for the purpose of confirming the 
model predictions. However, as of this printing, 
these analyses were not available. 

3.4.2.6 Model Application (Scenarios) 

The calibration of the model was undertaken using 
three scenarios (1, 2, and 3) that included lower 
boundary conditions, upper boundary conditions, and 


The scenarios 4 through 7 are referred to as load 
reduction scenarios. These are not necessarily 
management scenarios but can give managers 
insight as to which loads are important in the model 
and environment for the purpose of predicting 
concentrations of atrazine in the lake. It is believed 
that they bound the entire range of potential loads 
and provide some specific load scenarios within the 
range. Scenario 3 was used to simulate conditions 
from January 1,1996 through December 31,2004 for 
scenarios 4 through 7 described below. When 
December 31, 2004 is reached, each of the load 
reduction scenarios 4 through 7 began on January 1, 
2005 and were run for a period of 50 years. 

The Lake Superior boundary condition was assumed 
linear during the period modeled (0 ng/L at the 
beginning of year 1963 and 3.5 ng/L at 1994) and 
likewise for the Lake Huron boundary condition (0 
ng/L at the beginning of year 1963 and 23 ng/L at 
1992). The boundary conditions were assumed to be 
zero in 1963 because this was the year when the 
herbicide was first introduced to the basin. Lake 
Superior and Lake Huron atrazine concentrations for 
the years 1993 and 1992, respectively, were based 
on measurements of atrazine in these lakes 
(Schottler and Eisenreich, 1994). While the Lake 
Superior flow component of the return flow to Lake 
Michigan is primarily characteristic of concentrations 
of atrazine in Lake Superior, the actual concentration 
is probably somewhere between Lake Superior and 
Lake Michigan due to some mixing (see section 
3.3.2). 

1. Calibration Based on Upper Estimate of 
Boundary Conditions - The summer inflow 
concentration at the Straits of Mackinac was 
assumed to be 100% Lake Huron water. Lake 
Huron water started at 0 ng/L and was assumed 
to linearly rise to 23 ng/L as observed in 1995 
(Station 54) and then held constant at that level 
for the remainder of the simulation. Tributary 
loading projections were set equal to an 


90 



averageof loadings for 1995 and 1998, but prior 
to that time, the historical loading estimates were 
used. Wet deposition projections were set equal 
to an average of loads for 1978 through 1998. 
Wet deposition loadings before that were based 
on historical load estimates. The model was 
calibrated by adjusting the overall internal decay 
to best match whole-lake volume-weighted 
average concentration. 

2. Calibration Based on Lower Estimate of 
Boundary Conditions - The summer inflow 
concentration at the Straits of Mackinac was 
assumed to be 100% Lake Superior water. Lake 
Superior was assumed to begin with an atrazine 
concentration of 0 ng/L, was then assumed to 
linearly rise to 3.5 ng/L as observed in 1994 
(Schottler and Eisenreich, 1997), and was then 
held constant at that level for the remainder of 
the simulation. Tributary loading projections were 
set equal to an average of loadings for 1995 and 
1998, but prior to that time, the loading history 
estimates were used. Wet deposition projections 
were set equal to an average of loads for 1978 
through 1998. Wet deposition loadings before 
that time were based on historical load estimates. 
The model was calibrated by adjusting the overall 
internal decay to best match whole-lake volume- 
weighted average concentration. 

3. Calibration Based on “Average” Boundary 
Conditions - The inflow concentration at the 
Straits of Mackinac was assumed to be 50% 
Lake Superior and 50% Lake Huron water. This 
mix of water was assumed to begin with an 
atrazine concentration of 0 ng/L, was assumed to 
linearly rise to 13.25 or [(3.5+23)/2j ng/L in 1995, 
and was then held constant at that level for the 
remainder of the simulation. Tributary loading 
projections were set equal to an average of 
loadings for 1995 and 1998, but prior to that time, 
loading history estimates were used. Wet 
deposition projections were set equal to an 
average of loads for 1978 through 1998. Wet 
deposition loadings before that were based on 
historical load estimates. The model was 
calibrated by adjusting the overall internal decay 
to best match whole-lake volume-weighted 
average atrazine concentration. 


4. Virtual Elimination (Lower Bound on Model 
Prediction) - This scenario simulated a 100% 
reduction of tributary and atmospheric loads. For 
the projections, the Lake Huron/Superior 
boundary conditions were set equal to zero. This 
scenario was run using scenario 3 for predictions 
leading up to the date when the virtual elimination 
scenario was to take place. 

5. No Tributary Loads - This scenario simulated a 
100% reduction of tributary loadings. Wet 
deposition loads were set equal to an average of 
loads for 1978 through 1998. This scenario was 
run using scenario 3 for predictions leading up to 
the date when the 100% tributary load reduction 
scenario began. 

6 . No Wet Atmospheric Deposition Loadings - 

Tributary loads were set equal to an average of 
loadings for 1995 and 1998. Atmospheric wet 
deposition loadings were decreased by 100%. 
This scenario was run using scenario 3 for 
predictions leading up to the date when the 100% 
atmospheric load reduction scenario began. 

7. No Further Degradation of Lake Water Quality 

- A total load (tributary and wet deposition) was 
determined such that no further increase in lake¬ 
wide volume-weighted concentration was 
observed starting in January 1,2005. Up through 
December 31,2004, scenario 3 was used. 

3.4.2.7 Discussion of Results 

Total internal degradation of atrazine in the water (k d ) 
determined by model calibration was low in all 
scenarios where evaluated (see Figures 3.4 and 3.5). 
These rates of decay for scenarios 1,2, and 3 were 
0.0125/yr, 0.008/yr, and 0.01 /yr, respectively. For 
the calibration based on average boundary 
conditions, MICHTOX predicts that approximately 1 % 
of the atrazine in the lake decays each year due to 
some combination of abiotic and biotic decay in the 
lake. 

Decay can be related to the half-life of the chemical 
in the lake by the following: 

Half-Life = t V2 ={ln2)/k d (3.1) 


91 





70 


c 



Jan 1 Sept. 9 May19 Jan. 26 Oct. 4 June 13 Feb. 19 Oct. 29 

1963 1976 1990 2004 2017 2031 2045 2058 

date 

Figure 3.4. Lake Michigan (open-lake) forecast scenarios: 1 - upper estimate of boundary condition, 
2 - lower estimate of boundary condition, and 3 - estimate of average boundary condition. 



Jan. 1 Sept. 9 May19 Jan, 26 Oct. 4 June 13 Feb 19 Oct 29 

1963 1976 1990 2004 2017 2031 2045 2058 

date 

Figure 3.5. Lake Michigan (open-lake) hindcast and scenario forecasts: 4 - virtual elimination of all 
loadings and 0.0 ng/L atrazine at the Straits of Mackinac boundary, 5 - no tributary loads, 6 - no wet 
deposition, 7 - no further degradation of lake water quality. 


92 





























































Assuming that scenario 3 captures typical conditions, 
then the 1% internal decay associated with this 
scenario represents a half-life of the chemical in the 
lake of 69.3 years. In scenario 3, the water at the 
Straits of Mackinac was assumed to be half Lake 
Superior water and half Lake Huron water and is 
believed to be a fair assessment of the conditions 
during summer stratification. It is intuitive that the 
decay rate associated with scenario 1 that has the 
highest boundary condition concentrations of atrazine 
(assumed to be all Lake Huron water) is the one with 
the highest decay rate because higher boundary 
concentrations will mean that more atrazine is 
transported into the lake at the Straits of Mackinac. 
This higher loading will result in a higher decay 
needed in the modeling calibration exercise in order 
for model output to match observations. The 
opposite argument is true for the scenario where the 
boundary condition at the Straits of Mackinac is 
based solely on the lower concentrations of atrazine 
from Lake Superior. 

In the forecasts of alternate futures (Figure 3.5), 
eliminating all loadings to the lake resulted in the 
largest atrazine decline in model predictions. A total 
loading reduction of approximately 37%, if 
implemented on January 1,2005, would be needed 
in order to prevent atrazine concentrations from 
increasing higher than what was estimated in the 
lake on January 1, 2005. If only the atmospheric 
loadings ceased (scenario 6), concentrations would 
continue to increase. However, if only the tributary 
loadings ceased (scenario 5), concentrations in the 
lake would decline relative to scenario 3 predictions. 

References 

Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W. 
Schanz. 1988. WASP4, A Hydrodynamic and 
Water Quality Model - Model Theory, User’s 
Manual, and Programmer’s Guide. U.S. 
Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory, Athens, Georgia. 
EPA/600/3-87/039, 297 pp. 


Ayers, J.C., D.V. Anderson, D.C. Chandler, and G.H. 
Lauff. 1956. Currents and Water Masses of 
Lake Huron (1954 Synoptic Surveys). The 
University of Michigan, Great Lakes Research 
Institute, Ann Arbor, Michigan. Technical Paper 
Number 1, 101 pp. 

Brent, R.N., J. Schofield, and K. Miller. 2001. 
Results of the Lake Michigan Mass Balance 
Study: Atrazine Data Report. U.S. 

Environmental Protection Agency, Great Lakes 
National Program Office, Chicago, Illinois. EPA/ 
905/R-01/010, 92 pp. 

Endicott D.D., W.L. Richardson, and D.J. Kandt. 
2005. 1992 MICHTOX: A Mass Balance and 

Bioaccumulation Model for Toxic Chemicals in 
Lake Michigan, Part 1. In: R. Rossmann (Ed.), 
MICHTOX: A Mass Balanceand Bioaccumulation 
Model for Toxic Chemicals in Lake Michigan. 
U.S. Environmental Protection Agency, Office of 
Research and Development, National Health and 
Environmental Effects Research Laboratory, Mid- 
Continent Ecology Division, Large Lakes 
Research Station, Grosse lie, Michigan. 
EPA/600/R-05/158, 140 pp. 

Goolsby, D.A., E.M. Thurman, M.L. Pomes, M. 
Meyer, and W.A. Battaglin. 1993. Occurrence, 
Deposition, and Long Range Transport of 
Herbicides in Precipitation in the Midwestern and 
Northeastern United States. In: D.A. Goolsby, 
L.L. Boyer, and G.E. Mallard (Eds.), Selected 
Papers on Agricultural Chemicals in the Water 
Resources of the Midcontinental United States, 
pp. 75-89. U.S. Geological Survey, Denver, 
Colorado. Document Number: 93-418, 89 pp. 

Martin, S.C., S.C. Hinz, P.W. Rodgers, V.J. Bierman, 
Jr., J.V. DePinto, and T.C. Young. 1995. 
Calibration of a Hydraulic Transport Model for 
Green Bay, Lake Michigan. J. Great Lakes Res., 
21 (4):599-609. 

Quinn, F.H. 1977. Annual and Seasonal Flow 
Variations Through the Straits of Mackinac. 
Water Resources Res., 13(1 ):137-144. 

Quinn, F.H. 1992. Hydraulic Residence Times for 
the Laurentian Great Lakes. J. Great Lakes 
Res., 18(1 ):22-28. 


93 






Rodgers, P.W. and D.K. Salisbury. 1981. Water 
Quality Modeling of Lake Michigan and 
Consideration of the Anomolous Ice Cover of 
1976-1977. J. Great Lakes Res., 7(4):467-480. 

Rygwelski, K.R., W.L. Richardson, and D.D. Endicott. 
1999. A Screening-Level Model Evaluation of 
Atrazine in the Lake Michigan Basin. J. Great 
Lakes Res. 25(1 ):94-106. ' 

Schelske, C.L., E.F. Stoermer, J.E. Gannon, and 
M.S. Simmons. 1976. Biological, Chemical, and 
Physical Relationships in the Straits of Mackinac. 
U.S. Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory-Duluth, Large Lakes 
Research Station, Grosse lie, Michigan. 
EPA/600/3-76/095, 267 pp. 

Schottler, S.P. and S.J. Eisenreich. 1997. Mass 
Balance Model to Quantify Atrazine Sources, 
Transformation Rates, and Trends in the Great 
Lakes. Environ. Sci. Technol., 31 (9):2616-2625. 


Schottler, S.P. and S.J. Eisenreich. 1994. 
Herbicides in the Great Lakes. Environ. Sci. 
Technol., 28(12):2228-2232. 

Thomann, R.V. and J.A. Mueller. 1987. Principles of 
Surface Water Quality Modeling and Control. 
Harper Collins Publishers, Inc., New York, New 
York. 

Thomann, R.V., R.P. Winfield, and J.J. Segna. 1979. 
Verification Analysis of Lake Ontario and 
Rochester Embayment Three-Dimensional 
Eutrophication Models. U.S. Environmental 
Protection Agency, Office of Research and 
Development, Environmental Research 
Laboratory-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. EPA/600/3-79- 
094, 136 pp. 

U.S. Department of Agriculture. 2001. Agriculture 
Research Service Pesticide Properties. Available 
from U.S. Department of Agriculture at 
http://www.ars.usda.gov. 


94 





PART 4 






LAKE MICHIGAN MASS BALANCE PROJECT 
LEVEL 2 MODEL: LM2-ATRAZINE 


Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research Branch 

and 

Xiaomi Zhang 

Z-Tech, an ICF International Company 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 


4.1 


LM2-Atrazine Executive Summary 


LM-2 Atrazine was run in a hindcast and forecast 
mode under various load and modified boundary 
condition scenarios. A calibration run, based on 
averageboundary conditionsusing historical loadings 
of atrazine to Lake Michigan, suggests that only 0.9% 
of the atrazine in the lake decays each year. Net 
volatilization of atrazine is negligible. Tributaries, 
transporting the atrazine run-off load from farm fields, 
contribute most of the atrazine load to Lake 
Michigan. In the forecasts of alternate futures, 
eliminating all loadings to the lake resulted in the 
largest decline in model predictions. A total load 
reduction of approximately 35%, if implemented on 
January 1,2005, would have been needed in order 
to prevent atrazine concentrations from increasing 
above those that were estimated in the lake on 
January 1,2005. 


4.2 LM2-Atrazine Recommendations 

Due to its fast run-time speed, LM2-Atrazine can be 
used to perform long-term model forecasts of lake 
concentrations. As additional loading data become 
available, the updated loading history can easily be 
added to existing model input files. For additional 
model confirmation purposes, it is recommended that 
the model predictions be compared to data from lake 
samples that were collected in 2005 when these data 
become available. 

4.3 Model Description 

4.3.1 Model Overview 

As one of the models in the Lake Michigan Mass 
Balance Project (LMMBP), LM2-Toxic was 
specifically developed to simulate the transport and 
fate of hydrophobic toxic chemicals, such as 
polychlorinated biphenyl (PCB) congeners, in both 


95 






the water and sediment of Lake Michigan (Zhang, 
2006). LM2-Toxic is a descendant of the WASP4 
water quality modeling framework (Ambrose et al., 
1988). The model solves mass balance equations 
based on a finite volume spatial discretization 
(Thomann and Mueller, 1987) and Euler time 
integration. Compared to WASP4, LM2-Toxichasan 
updated air-water exchange formulation that includes 
a Henry’s law temperature-corrected coefficient as 
described by Bamford et al. (1999); water phase 
mass transfer coefficient per Wanninkhoff et al. 
(1991); and the air phase mass transfer coefficient by 
Schwarzenbach etal. (1993). The LM2-Toxic model 
is capable of incorporating a full range of transport 
and fate processes such as advection, dispersion, 
particle settling, sediment resuspension, sediment 
burial, transport in sediment pore water, partitioning 
to particles in the water column and sediment, 
reaction, volatilization, and gas absorption. 

The LM2-Atrazine model is identical to LM2-Toxic 
except for differences in the volatilization algorithms. 
In LM2-Atrazine, the algorithm for calculating the 
temperature-dependent Henry’s law coefficient 
follows that of Scholtz et al. (1999) and Miller (1999). 
The dimensionless valueforthe Henry’s law constant 
was set to 8.1 x 10' 8 (U.S. Department of Agriculture, 
2001). The water and air phase mass transfer 
coefficients were that of the O’Connor “long form’’ 
and O’Connor, respectively (O’Connor, 1983). The 
volatilization algorithm differences between LM2- 
Toxic and LM2-Atrazine would not be expected to 
have a significant impact on atrazine model 
predictions because of the low value of the Henry’s 
law constant for atrazine. As a non-hydrophobic 
chemical, atrazine was not associated with 
particulates in LM2-Atrazine. Therefore, processes 
such as resuspension, settling, burial in sediment, 
transport in sediment pore water, and partitioning to 
solids in the water column and sediment were not 
operative. Processes such as advection, dispersion, 
reaction, volatilization, and gas absorption were 
active. For information on the physical and chemical 
properties of atrazine, see Part 1, Chapter 2. 

In a manner similar to MICHTOX (see Part 3), LM2- 
Atrazine was used in a hindcast mode to simulate 
atrazine concentrations in Lake Michigan and Green 
Bay in response to mass loadings to those systems 
from the time of introduction in 1964 up to 1995. The 
calibrated model was then used in a forecast mode 


to predict lake-wide atrazine concentrations in Lake 
Michigan as a function of various loading scenarios. 

4.3.2 LM2-Atrazine Model Segmentation 
and Circulation 

Compared to MICHTOX (Level 1 contaminant 
transport and fate model developed for Lake 
Michigan) segmentation (Figure 1.5.1), the LM2- 
Atrazine model has a finer resolution (Figure 4.1). 
Most water column segments in the LM2-Atrazine 
model segmentation schematic share the same or 
portions of the segment boundaries used in the 
MICHTOX atrazine model. The spatial segmentation 
for the LM2-Atrazine model was developed from 
digitized bathymetric (5 km x 5 km grid) and shoreline 
data for Lake Michigan provided by Dr. David 
Schwab, National Oceanic and Atmospheric 
Administration (NOAA) (Schwab and Beletsky, 1998). 
The lake, including Green Bay, was divided into 10 
horizontal columns, five water column layers, and 
one surficial sediment layer. A detailed spatial and 
cross sectional display of the water segments for 
LM2-Atrazine is illustrated in Figure 4.1. There are 
41 segments in total. Segments 1-10 are surface 
water segments with an interface with the 
atmosphere. The rest of the segments lie below 
these surface segments. 

Water balance is one of the major components in a 
traditional water quality modeling framework. Water 
movement directly controls the transport of solids and 
chemicals in dissolved and particulate phases in a 
water system. In terms of LM2-Atrazine model 
inputs, the data in the transport fields such as 
advective flows and dispersive exchanges, or mixing, 
were used to describe the water balance in the 
model. The components and their sources used in 
LM2-Atrazine model transport fields are listed below: 

1. Bi-direction horizontal advective flows (provided 
by David Schwab, NOAA; originally based on 
Schwab and Beletsky (1998). 

2. Net vertical advective flows (provided by David 
Schwab, NOAA; originally based on Schwab and 
Beletsky (1998). 

3. Tributary flows and bi-directional flows across the 
Straits of Mackinac (Endicott et al., 2005; Quinn, 
1977). 


96 




Figure 4.1. Water column segmentation for LM2- 
Atrazine. 

4. Water balancing flows. 

5. Vertical dispersion coefficients. 

Components such as precipitation, evaporation, and 
groundwater infiltration were not considered in the 
water transport fields used in the LM2-Atrazine 
model. 


Correct water circulation is essential for the accuracy 
of outputs from the LM2-Atrazine model. The 
Princeton Ocean Model (POM) has been 
demonstrated to accurately simulate water 
movement for a given large water body (Schwab and 
Beletsky, 1997; Blumberg and Mellor, 1987). Using 
an extensively tested version of POM for the Great 
Lakes (POMGL), transport fields were generated for 
Lake Michigan at different spatial and temporal 
resolutions for use in a series of mass balance 
models adapted for LMMBP (Schwab and Beletsky, 
1998). The hydrodynamic model for Lake Michigan 
had 20 vertical layers and a uniform horizontal grid 
size of 5 km x 5 km (Schwab and Beletsky, 1998). 
Because the LM2-Atrazine model segmentation was 
constructed based on the 5x5 km 2 grid used in the 
POMGL for Lake Michigan, the hydrodynamic model 
results were relatively easily aggregated to the 
resolution used in LM2-Atrazine (Schwab and 
Beletsky, 1998). The aggregated horizontal bi¬ 
direction flows at each interface provided a good 
approximation of horizontal advective and dispersive 
transport components at the interface. The 
advantage of using bi-directional flows at an interface 
was that it bypassed the tedious and necessary 
horizontal dispersion coefficient calibration 
procedure required when only net flow is available at 
the interface. 

The vertical transport field was calculated in the form 
of net vertical flow [provided by David Schwab, 
NOAA and originally based on Schwab and Beletsky 
(1998)]. Therefore, vertical exchange coefficients 
were calculated and calibrated to define the vertical 
mixing process between vertically adjacent 
segments. A summer period of strong stratification 
and a non-stratified period of intense vertical mixing 
are important limnological features of the Great 
Lakes (Chapra and Reckhow, 1983; Thomann and 
Mueller, 1987). Therefore, determining the dynamics 
of vertical mixing was considered an important model 
development task for the LMMBP. 

A thermal balance model was constructed to 
calibrate the vertical exchange coefficients at the 
interfaces (Zhang et al., 1998, 2000). The 
coefficients were calibrated using 250 observed 
vertical temperature profiles collected at 40 stations 
in Lake Michigan during the 1994-1995 LMMBP 
period (Zhang, 2006). 


97 


































































































































Water balancing flow was another advective 
component added into the water transport field for 
LM2-Atrazine. The aggregated advective flows 
provided by NOAA were not balanced in individual 
segments over the two-year LMMBP period. 
However, the total water mass was perfectly 
balanced on a whole - lake basis. Over the two-year 
LMMBP period, some segments lost or gained a 
certain amount of water. This problem could be very 
significant for long-term simulations for the LM2- 
Atrazine model because the model simulation stops 
once the volume of a segment reaches zero. To 
counter the amount lost or gained in each segment, 
a water balancing flow was introduced to keep the 
volume of water unchanged in each segment at any 
time during the simulation. The balancing flows were 
generated based on the aggregated advective flows 
[provided by David Schwab, NOAA, and originally 
based on Schwab and Beletsky (1998)], original 
volume of each segment, and the general water 
circulation patterns during the LMMBP period. 

Tributary flows and flows through the Straits of 
Mackinac were based on MICHTOX model inputs 
(Endicott et ai, 2005), the literature (Quinn, 1977), 
and water circulation patterns during the LMMBP 
period [provided by David Schwab, NOAA, and 
originally based on Schwab and Beletsky (1998)]. 
During a period of approximately 100 days in the 
summer, flow and exchange across the Straits of 
Mackinac occurs in two discrete layers formed by the 
surface water and deep, cold, hypolimnetic water. 
During this period of stratification, surface layer flow 
is from Lake Michigan to Lake Huron, and a deeper 
return flow to Lake Michigan is observed. It has been 
observed that Lake Superior water discharging from 
the St. Marys River travels in a persistent westerly 
direction during stratification and constitutes a 
significant component of the return flow to Lake 
Michigan (Ayers etal., 1956; Schelske eta!., 1976; J. 
Saylor, NOAA, personal communication, 1998). The 
remainder of this return flow to Lake Michigan is Lake 
Huron water. 

Hydraulic residence times (volume/outflow) for the 
main lake has been estimated to be 62 years (Quinn, 
1992). 

After vertical exchange coefficients were calibrated, 
a conservative constituent, chloride, was simulated 
using the LM2 model configuration to verify that the 


water transport components described above were a 
good representation of the overall water transport 
field for atrazine. The chloride model was run just 
once without adjusting any parameters or 
coefficients. The model results agreed very well with 
the observations during the LMMBP period (Zhang, 
2006). 

Water column concentration profiles of atrazine at 10 
open-lake stations representing four to 10 depths per 
station showed no vertical gradients during lake 
stratification for the years 1991-1992 (Schottler and 
Eisenreich, 1997) and 1994-1995 (Brent etal., 2001). 
Furthermore, Schottler and Eisenreich reported that 
analysis of data from their 10 lake stations that 
covered a central north-south axis and an east-west 
axis showed no horizontal gradients of atrazine 
concentrations in the lake. 

4.4 LM2-Atrazine Model Application to 
Lake Michigan 

4.4.1 Enhanced Screening Model 
Application 

For the LM2-Atrazine model runs, seven years of 
atrazine application data were available and used. 
Also, due to label changes that lowered application 
amounts and established planting setbacks from 
water bodies in 1990 and 1992, a decision was made 
to use two tributary load ratios in order to address 
atrazine application practices for pre- and post-label 
changes. New data from the LMMBP also became 
available to modelers. With these additional data, 
loading ratios for both the tributaries and wet 
deposition were updated (see Part 2, Chapters 2 and 
3). The model was calibrated by determining a total 
decay that would yield a best fit of the model to 
observations in the lake. Also, several forecasting 
scenarios were run with the model. The efforts of 
this additional modeling are described in the following 
sections of this part. 

4.4.2 Field Data 

See Part 1, Chapter 3 for atrazine field data from the 
lake, tributaries, and atmospheric components. 


98 



4.4.3 Tributary Loadings 

It was assumed that a Watershed Export Percentage 
(WEP) of 0.6% derived from the literature for 
fine/moderate textured soils adequately described 
the overall WEP of the Lake Michigan watershed. 
This WEP, along with historical annual atrazine 
usage in the United States, was used to calculate 
atrazine loadings from the tributaries to the lake. For 
a complete discussion on the WEP method used to 
estimate LM2-Atrazine loadings, please see Part 2, 
Chapter 2. 

The Stratified Beale Ratio Estimator (SBRE) method 
was used to estimate tributary loads in the 11 
monitored tributaries during the LMMBP utilizing 
tributary flow and concentration data. Also, 
estimates of loads from the unmonitored watersheds 
were made. However, loads were apparently 
missed, and therefore, LM2-Atrazine tributary loads 
were based only on WEP, county-level application 
data and total United States annual usage records. 
See Section 5.3.3.3.1 for a discussion of this topic. 

4.4.4 Atmospheric Loadings 

Loading estimates of wet deposition to Lake 
Michigan and Green Bay were made for each of the 
top surface water segments. These loadings were 
estimated for LM2-Atrazine per the discussion in Part 
2, Chapter 3. 

4.4.5 Model Assumptions 

Model processes involving sediments and 
particulates in the water column were not included in 
the LM2-Atrazine model runs because atrazine is 
primarily in the dissolved state in surface waters; 
therefore, any processes that involve sediment or 
suspended particle interactions were concluded to be 
of minor significance (Section 1.2.2). 

A literature review of atrazine degradation processes 
in surface freshwater presented in Part 1, Chapter 2 
suggests that degradation is hindered in freshwaters 
such as in Lake Michigan where the water is cold, 
has low solids concentrations, low dissolved organic 
carbon, a high pH, and low concentration of nitrate 
ions. Degradation of atrazine is known to occur 
through either biotic or abiotic processes in some 
environmental compartments. Given the lack of any 


Lake Michigan-specific kinetic information on any of 
these processes, the approach taken in LM2-Atrazine 
was to estimate the loading history of atrazine to the 
lake and then find an overall first-order loss rate 
constant to fit the model to observations of atrazine 
in the lake water. 

Therefore, due to atrazine’s physical and chemical 
properties (Part 1, Chapters 2 and 3), processes 
modeled included only advection, dispersion, 
volatilization, absorption, and reaction (atrazine 
decay). 

4.4.6 Model Calibration and Application 
(Scenarios) 

The calibration of the model was undertaken using 
three scenarios (1, 2, and 3) that included lower 
boundary condition, upper boundary condition, and a 
most likely or average boundary condition scenario, 
respectively. All of these model runs started on 
January 1, 1963 with a zero load. The model was 
calibrated by finding an appropriate internal decay 
until the model output best matched the observed 
atrazine concentration in the lake for samples taken 
in 1991, 1992, 1994, and 1995. 

The scenarios 4 through 8 are referred to as load 
reduction scenarios. These are not necessarily 
management scenarios, but they can give managers 
insight as to which loads are important in the model 
and environment for the purpose of predicting 
concentrations of atrazine in the lake. It is believed 
that they provide bounds on the entire range of 
potential loads. Scenario 3 was used to simulate 
conditions from January 1, 1996 through December 
31, 2004. Then on January 1, 2005, the load 
reduction scenarios 4 through 8 began and ran for a 
period of 50 years. 

Scenario 1 - Calibration Based on an Upper 
Estimate of Boundary Conditions: In this 

scenario, the initial vapor phase concentration was 0 
ng/m 3 and increased linearly until December 31, 
1977. Starting on January 1, 1978, the vapor phase 
concentration was held constant at the atrazine 
detection limit of 0.00926 ng/m 3 (Miller, 1999) 
throughout the remainder of the simulation period. 
The summer inflow concentration at the Straits of 
Mackinac was assumed to be 100% Lake Huron 
water. Lake Huron water was initially set at 0 ng/L 


99 









and was assumed to rise linearly to 23 ng/L observed 
in 1995 (Station 54), and then remained constant for 
the remainder of the simulation. Tributary loading 
projections were set equal to an average of loadings 
for 1995 and 1998, but prior to that time, the 
historical loading estimates were used. Wet 
deposition loads beyond 1998 were set equal to an 
average of loads 1978 through 1998. Wet deposition 
loadings before that were based on historical load 
estimates. Volatilization, absorption, and other 
processes were active in the model. An internal 
decay was then selected for the model run that 
yielded a best fit to whole-lake volume-weighted 
average concentrations. 

Scenario 2 - Calibration Based on a Lower 
Estimate of Boundary Conditions: In this 

scenario, the vapor phase concentration was initially 
set at 0 ng/m 3 and remained at that concentration for 
the entire simulation period. The summer inflow 
concentration at the Straits of Mackinac was 
assumed to be 100% Lake Superior water. Lake 
Superior water was initially set at 0 ng/L, and was 
assumed to rise linearly to 3.5 ng/L observed in 
1994, and then held constant at that level for the 
remainder of the simulation. Tributary loading 
projections were set equal to an average of loadings 
for 1995 and 1998, but prior to that time, the 
historical loading estimates were used. Wet 
deposition projections beyond 1998 were set equal to 
an average of loads for 1978 through 1998. Wet 
deposition loadings before that were based on 
historical load estimates. Volatilization, absorption, 
and other processes were active in the model. An 
internal decay was then selected for the model run 
that yielded a best fit to whole-lake volume-weighted 
average concentrations. 

Scenario 3 - Calibration Based on “Average” 
Boundary Conditions: In this scenario, the vapor 
phase concentration was initially set at 0 ng/m 3 and 
then increased linearly up to 0.00463 ng/m 3 (one-half 
detection limit) until December 31,1977. Starting on 
January 1,1978, this vapor phase concentration was 
held constant at 0.00463 ng/m 3 throughout the 
remainder of the simulation period. The inflow 
concentration at the Straits of Mackinac was 
assumed to be 50% Lake Superior and 50% Lake 
Huron water. This mix of water started out at 0 ng/L 
and was assumed to linearly rise to 13.25 ng/L or ( V 2 
x (3.5+23)) ng/L as observed in 1995 in Lake 


Superior and Lake Huron, respectively, and then held 
constant at that level for the remainder of the 
simulation. Tributary loading projections were set 
equal to an average of loadings for 1995 and 1998, 
but prior to that time the variable loading estimates 
were used. Wet deposition projections beyond 1998 
were set equal to an average of loads for 1978 
through 1998. Wet deposition loading before that 
were based on historical load estimates. 
Volatilization, absorption, and other processes were 
active in the model. An internal decay was then 
selected for the model run that yielded a best fit to 
whole-lake volume-weighted average concentrations. 

Scenario 4 - Virtual Elimination (Lower Bound on 
Model Predictions): In this scenario, tributary and 
atmospheric loads were reduced by 100%. For the 
projections, vapor phase concentrations and the 
Lake Huron/Superior boundary conditions were set to 
zero. All modeling processes were active. This 
scenario was run using scenario 3 for predictions 
leading up to the date when the virtual elimination 
scenario began (January 1,2005). 

Scenario 5 - No Tributary Loads: In this scenario, 
the tributary loadings were reduced by 100%. Wet 
deposition loads were set equal to an average of 
loads for 1978 through 1998. This scenario was run 
using scenario 3 for predictions leading up to the 
date when the 100% tributary load reduction scenario 
was began (January 1, 2005). All other modeling 
processes were active. 

Scenario 6 - No Wet Atmospheric Deposition 
Loadings: Tributary loads were set equal to an 
average of loadings for 1995 and 1998. Atmospheric 
wet deposition loadings were decreased by 100%. 
This scenario was run using scenario 3 for 
predictions leading up to the date when the 100% 
atmospheric load reduction scenario began (January 
1,2005). All other modeling processes were active. 

Scenario 7 - Zero Vapor Phase Concentration: 

Tributary loads were set equal to an average of 
loadings for 1995 and 1998. Wet deposition loads 
were set equal to an average of loads for 1978 
through 1998. Vapor phase concentration were set 
equal to zero. This scenario was run using scenario 
3 for predictions leading up to the date when the 
zero vapor phase concentration scenario began 


100 





(January 1, 2005). All other modeling processes 
were active. 

Scenario 8 - No Further Degradation: A total load 
(tributary and wet deposition) was determined using 
the model such that no further increase in lake-wide 
volume-weighted concentration would be observed 
after January 1, 2005. Up through December 31, 
2004, scenario 3 was used. 

4.4.7 Model Confirmation 

In 2005, atrazine water samples were collected in 
Lake Michigan for the purposes of confirming the 
model predictions. However, as of this printing, 
these analyses were not available. 

4.4.8 Discussion of Results 

In terms of mass flow rates, LM2-Atrazine results 
from scenario 3 are depicted in Figure 4.2 for 1994. 
As shown, the highest load to the lake is from the 


tributaries followed by the load from the atmosphere 
in the form of wet deposition. The greatest loss of 
atrazine from the system is via export through the 
Straits of Mackinac. Loss due to internal decay is the 
second highest loss mechanism in the lake. 
Volatilization and gas absorption are minor processes 
in terms of mass flow gain and loss. 

Total internal degradation of atrazine in the water (k d ) 
determined by model calibration was low in all 
scenarios evaluated. These rates of decay for 
scenarios 1, 2, and 3 were 0.012/yr, 0.004/yr, and 
0.009/yr, respectively. For the calibration based on 
average boundary conditions (scenario 3), LM2- 
Atrazine predicts that approximately 0.9% of the 
atrazine in the lake decays each year due to some 
combination of abiotic and biotic decay in the lake. 
Decay can be related to the half-life of the chemical 
in the lake by the following: 

Half-Life = t 1/2 = (In 2)/k d (4.1) 




absorption 
231 kg/yr 


volatilization 
51 kg/yr A 





export via 
Chicago 
Diversion 
145 kg/yr 


atmospheric 
wet deposition 
2493 kg/yr 

sj? 


f T— - . 







input from 
Lake Huron 
472 kg/yr 


loss to decay: 1648 kg/yr 




watershed 
loading 
5264 kg/yr 


export to 
Lake Huron 
2531 kg/yr 


Atrazine Inventory 
182,779 kg 


Dry deposition, settling, sediment resuspension 

and net burial are negligible 


Figure 4.2. LM2-Atrazine model results for Lake Michigan and Green Bay for the year 1994. 


101 





















Assuming that scenario 3 captures typical conditions, 
then the 0.9% internal decay associated with this 
scenario represents a half-life of the chemical in the 
lake of 77 years. In scenario 3, the water at the 
Straits of Mackinac is assumed to be half Lake 
Superior water and half Lake Huron water. It is 
intuitive that the decay rate associated with scenario 
1 that has the highest boundary concentrations of 
atrazine (assumed to be all Lake Huron water) is the 
one with the highest decay rate because higher 
boundary concentrations will mean that more 
atrazine is transported into the lake at the Straits of 
Mackinac. Furthermore, the increased vapor phase 
concentration in scenario 1 will also contribute slightly 
more to gas absorption than the other scenarios. 
This cumulative higher mass flow will result in a 
higher decay needed in the modeling calibration 
exercise in order for model output to match lake 
concentration observations. The opposite argument 
is true for scenario 2 where the boundary condition at 
the Straits of Mackinac is based solely on the lower 
concentrations of atrazine from Lake Superior, and 
the vapor phase concentration of atrazine is 
assumed to be equal to zero throughout the entire 
simulation. 

In the forecasts of alternate futures (Figure 4.3), 
constant conditions scenario 3 results in lake 


concentrations increasing until a value of 
approximately 66 ng/L is attained. Scenario 3 is 
based on average boundary conditions, and the 
forecasts using this scenario are based on constant 
loadings that were observed in the mid to late 1990's. 
Eliminating all loadings to the lake (scenario 4) 
resulted in the largest decline in model predictions. 
A total loading reduction of approximately 35% 
(scenario 8), if implemented on January 1, 2005, 
would be needed in order to prevent atrazine 
concentrations from increasing further than what was 
estimated in the lake on January 1,2005. If only the 
atmospheric loadings ceased (scenario 6), then 
concentrations in the lake would not be expected to 
change much after January 1,2005, and the model- 
predicted concentrations in the lake would be 
expected to be only slightly higher than that predicted 
by scenario 8. However, if only the tributary loadings 
ceased (scenario 5), then atrazine concentrations in 
the lake would decline relative to scenario 3 
predictions. Maintaining the vapor phase 
concentration at 0 ng/l (scenario 7) has very little 
effect compared to the constant condition scenario 3. 
This is intuitive because scenario 3 vapor phase 
concentrations are set to one-half the detection limit 
of atrazine in the vapor phase. 


—I 

00 CO 

o o 
1 1 

c 

70 - 

c 

o 

60 - 

03 

W— 

C 

50 - 

<13 

O 

C 

o 

40 - 

O 

<D 

30 - 

c 

N 

a: 

V- 

20 - 

< 

10 - 


I Field data (+/- 1 standard deviation) 

— Constant loads (scenario 3) 

- Zero vapor phase concentration (scenario 7) 
No wet deposition (scenario 6) 

— 35% Reduction of total load (scenario 8) 

- 100% Reduction of tributary load (scenario 5) 

— 100% Reduction of total load (scenario 4) 


66 ng/L> 



1963 


2203 2233 2263 


Figure 4.3. LM2-Atrazine model runs of scenarios. 


102 

















Related to the production of ethanol for motor 
vehicles in this country, the demand for corn 
increased the United States corn acreage planted in 
2007 to 93.6 million acres, exceeding the acreage 
planted in 2006 by 19.5 % (U.S. Department of 
Agriculture, 2007). This also represents an increase 
of 24.5% of the average acreage planted during the 
project period, 1994-1995 (see Figure 4.4 for corn 
acreage in the United States from 1986 to 2007). 
This was the largest amount of corn planted in the 
United States since 1944 when farmers planted 95.5 
million acres. It can be assumed that this increase in 
corn acreage has resulted in an increase in the use 
of atrazine in the Lake Michigan watershed. To 
estimate the potential impact on this increased usage 
of atrazine in the Lake Michigan basin, both 
atmospheric and tributary loadings were increased by 
15% and 30% starting in 2007 in scenario 3 (see 
Figure 4.5). For these increases, the lake reaches 


steady-state at approximately 75.2 ng/L and 84.2 
ng/L, respectively. At the time of this printing, data on 
the actual usage amounts of atrazine applied to the 
Lake Michigan basin were not available. Thus the 
range of percent increases for the basin is probably 
the best current estimate of the potential impact of 
increased loadings to the lake. 

In conclusion, the net volatilization of atrazine is 
negligible in Lake Michigan. Furthermore, model 
calibration over a hindcast suggests that very little of 
the atrazine inventory in the lake decays each year. 
The chemical almost behaves as a conservative 
substance in the cold, deep waters of Lake Michigan. 
If loadings stay that same or increase over what was 
observed in the 1990s, then the lake concentration of 
atrazine is expected to increase. 







U.S. Corn Acres 

Million Acres 



-o Planted -D-Harvested 


USDA-NASS 

10-12-07 


Figure 4.4. Historical trends of United States corn acreage planted and harvested from 1986 to 2007 
(U.S. Department of Agriculture, 2007). 


103 


















Year 


Figure 4.5. Model-predicted lake-wide averaged atrazine concentrations in water related to increases 
in atrazine loadings resulting from corn crop acreage increases are depicted. The actual Lake 
Michigan response is believed to be bracketed by the 15% to 30% total atrazine load increase to the 
lake related to the corn-to-ethanol biofuels program. 


References 

Ambrose, R.B., T.A. Wool, J.P. Connolly, and R.W. 
Shanz. 1988. WASP4, A Hydrodynamic and 
Water Quality Model - Model Theory, User’s 
Manual, and Programmer’s Guide. U.S. 
Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory, Athens, Georgia. 
EPA/600/3-87/039, 297 pp. 

Ayers, J.C., D.V. Anderson, D.C. Chandler, and G.H. 
Lauff. 1956. Currents and Water Masses of 
Lake Huron (1954 Synoptic Surveys). The 
University of Michigan, Great Lakes Research 
Institute, Ann Arbor, Michigan. Technical Paper 
Number 1, 101 pp. 

Bamford, H.A., J.H. Offenberg, R.K. Larsen, F.C. Ko, 
and J.E. Baker. 1999. Diffusive Exchange of 
Polycyclic Aromatic Hydrocarbons Across the Air- 
Water Interface of the Patapsco River, An 
Urbanized Subestuary of the Chesapeake Bay. 
Environ. Sci. Techno!., 33(13):2138-2144. 


Blumberg, A.F. and G.L. Mellor. 1987. A Description 
of a Three-Dimensional Coastal Ocean 
Circulation Model. In: N.S. Heaps (Ed.), Three- 
Dimensional Coastal Ocean Models, Coastal and 
Estuarine Sciences, pp. 1-16. American 
Geophysical Union, Washington, D.C. 

Brent, R.N., J. Schofield, and K. Miller. 2001. 
Results of the Lake Michigan Mass Balance 
Study: Atrazine Data Report. U.S. 

Environmental Protection Agency, Great Lakes 
National Program Office, Chicago, Illinois. 
EPA/905/R-01/010, 92 pp. 

Chapra, S.C. and K.H. Reckhow (Eds.). 1983. 

Engineering Approaches for Lake Management, 
Volume 2: Mechanistic Modeling. Ann Arbor 
Science Publishers, Ann Arbor, Michigan. 492 

pp. 


104 














Endicott, D.D., W.L. Richardson, and D.J. Kandt. 
2005. 1992 MICHTOX: A Mass Balance and 
Bioaccumulation Model for Toxic Chemicals in 
Lake Michigan. In: R. Rossmann (Ed.), 
MICHTOX: A Mass Balance and 

Bioaccumulation Model for Toxic Chemicals in 
Lake Michigan, Part 1. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and 
Environmental Effects Research Laboratory, 
Mid-Continent Ecology Division-Duluth, Large 
Lakes Research Station, Grosse lie, Michigan. 
EPA/600/R-05/158, 140 pp. 

Miller, S.M. 1999. Spatial and Temporal Variability 
of Organic and Nutrient Compounds in 
Atmospheric Media Collected During the Lake 
Michigan Mass Balance Study. M.S. Thesis, 
Department of Civil, Structural, and 
Environmental Engineering, State University of 
New York, Buffalo, New York. 181 pp. 

O’Connor, D.J. 1983. Wind Effects on Gas-Liquid 

( Transfer Coefficients. J. Environ. Engin., 
109(3):731-752. 

Quinn, F.H. 1977. Annual and Seasonal Flow 
Variations Through the Straits of Mackinac. 
Water Resources Res., 13(1 ):137-144. 

Quinn, F.H. 1992. Hydraulic Residence Times for 
the Laurentian Great Lakes. J. Great Lakes 
Res., 18(1 ):22-28. 

Schelske, C.L., E.F. Stoermer, J.E. Gannon, and 
M.S. Simmons. 1976. Biological, Chemical, and 
Physical Relationships in the Straits of Mackinac. 
U.S. Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory-Duluth, Large Lakes 
Research Station, Grosse lie, Michigan. 
EPA/600/3-76/095, 267 pp. 

Scholtz, M.T., B.J. Van Heyst, and A. Ivanhoff. 
1999. Documentation for the Gridded Hourly 
Atrazine Emissions Data Set for the Lake 
Michigan Mass Balance Study. U.S. 
Environmental Protection Agency, Office of 
Research and Development, National Exposure 
Research Laboratory, Research Triangle Park, 
North Carolina. EPA/600/R-99/067, 61 pp. 


Schottler, S.P. and S.J. Eisenreich. 1997. Mass 
Balance Model to Quantify Atrazine Sources, 
Transformation Rates, and Trends in the Great 
Lakes. Environ. Sci. Technol., 31 (9):2616-2625. 

Schwab, D.J. and D. Beletsky. 1997. Modeling 
Thermal Structure and Circulation in Lake 
Michigan. In: Estuarine and Coastal Modeling, 
pp. 511-522. Proceedings of the 5th International 
Conference of the American Society of Civil 
Engineers, Alexandria, Virginia. October 22-24, 
1997. 

Schwab, D.J. and D. Beletsky. 1998. Lake Michigan 
Mass Balance Study: Hydrodynamic Modeling 
Project. National Oceanic and Atmospheric 
Administration, Great Lakes Environmental 
Research Laboratory, Ann Arbor, Michigan. 
NOAA Technical Memorandum ERL GLERL-108, 
55 pp. 

Schwarzenbach, R.P., P.M. Gschwend, and D.M. 
Imboden. 1993. Environmental Organic 
Chemistry. John Wiley and Sons, Incorporated, 
New York, New York. 681 pp. 

Thomann, R.V. and J.A. Mueller. 1987. Principles of 
Surface Water Quality Modeling and Control. 
Harper Collins Publishers, Inc., New York, New 
York. 

U.S. Department of Agriculture. 2001. Agriculture 
Research Service Pesticide Properties Database. 
Available from U.S. Department of Agriculture at 
http://www.ars.usda.gov. 

U.S. Department of Agriculture. 2007. National 
Agricultural Statistics Service. U.S. Department 
of Agriculture, Washington, D.C. Available from 
U.S. Department of Agriculture at 
http://www.nass.usda.gov. 

Wanninkhoff, R., J.R. Ledwell, and J. Crusius. 1991. 
Gas Transfer Velocities on Lakes Measured with 
Sulfur Hexafluoride. In: S.C. Wilhelm and J.S. 
Culliver (Eds.), Air-Water Mass Transfer, pp. 441 - 
458. American Society of Civil Engineers, New 
York, New York. 


105 




Zhang, X., D. Endicott, and W. Richardson. 1998. 
Transport Calibration Model With Level 2 Model 
Segmentation Scheme. First Lake Michigan 
Mass Balance Project Science Panel Review, 
Southgate, Michigan. June 23, 1998. 12 pp. 

Zhang, X., W. Richardson, and K. Rygwelski. 2000. 
Preparation and Verification Transport Field for 
LMMBP Level 2 Contaminant: Transport and 
Fate Models. Second Lake Michigan Mass 
Balance Project Science Panel Review, 
Southgate, Michigan. September 27, 2000. 15 

pp. 


Zhang, X. 2006. LM-2 Toxic. In: R. Rossmann 
(Ed.), Results of the Lake Michigan Mass 
Balance Project: Polychlorinated Biphenyls 
Modeling Report, pp. 216-452. U.S. 
Environmental Protection Agency, Office of 
Research and Development, National Health and 
Environmental Effects Research Laboratory, Mid- 
Continent Ecology Division-Duluth, Large Lakes 
Research Station, Grosse lie, Michigan. 
EPA/600/R-04/167, 579 pp. 


106 



PART 5 


LAKE MICHIGAN MASS BALANCE PROJECT 
LEVEL 3 MODEL: LM3-ATRAZINE 


Timothy J. Feist 
Xiaomi Zhang 

Z-Tech, an ICF International Company 
Large Lakes Research Station 
and 

Kenneth R. Rygwelski 
William L. Richardson (Retired) 

Russell G. Kreis, Jr. 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 

Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research Branch 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 


5.1 LM3-Atrazine Executive Summary 

Most previous Great Lakes water quality models 
were developed using large spatial scales. These 
models were capable of predictions on a lake-wide or 
regional scale but were not suitable for evaluating 
differences on smaller spatial scales, such as 
between off-shore and near-shore concentrations. 
The LM3-Atrazine model is a high-resolution model 
that is suitable for evaluating fine-scale spatial and 
temporal changes in water quality. 

The LM3-Atrazine model was based upon the same 
framework as the United States Environmental 
Protection Agency’s (USEPA) other LM3 models. 
The hydrodynamic transport was provided by the 
National Oceanic and Atmospheric Administration s 


(NOAA) Great Lakes Princeton Ocean Model (POM). 
The water quality framework was the same as used 
by the LM3 chloride and eutrophication models. 
Water quality components for the atrazine model 
were developed at the USEPA Large Lakes 
Research Station (LLRS) and included a small first- 
order decay rate and volatilization. The model spatial 
resolution consisted of a 5 km x 5 km horizontal grid 
with 19 vertical layers, for a total of 44,042 model 
cells. The model was run using a time step of three 
hours. 

Tributary loads, atmospheric loads, and boundary 
conditions for the model were estimated as part of 
the Lake Michigan Mass Balance Project (LMMBP). 
Model simulations were conducted using tributary 
loads for the 1994-1995 LMMBP period estimated by 


107 








the United States Geological Survey (USGS) and 
alternative loads with a spring seasonal adjustment 
based upon long-term historical loading trends. 

The LM3-Atrazine model could not be fully calibrated 
because only one year of in-lake data and one year 
of tributary loading data were collected during the 
LMMBP. However, confidence in model results was 
provided by the favorable comparison of model 
results to available data without adjustment of kinetic 
parameters, by successful calibration of the 
hydrodynamic model, by successful calibration of the 
high-resolution model transport (in the form of a 
chloride model), and by the long-term hindcast 
calibrations of coarser segmented atrazine models 
using the same water quality kinetics. 

The high-resolution model was useful in 
demonstrating the effects of tributary loadings on 
near-shore waterquality. Predicted mid-lake atrazine 
concentrations varied annually less than 5 ng/L 
during the two-year simulations. In comparison, the 
model segment receiving loads from the largest 
tributary, the St. Joseph River, ranged from winter 
concentrations of 37 ng/L to spring peaks of 100-350 
ng/L depending upon whether 1994-1995 or long¬ 
term tributary loads were used in the simulation. 

The USEPA collected atrazine samples from Lake 
Michigan during the 2005 field season; however, the 
results were not available at the time the atrazine 
modeling was conducted. To estimate potential 
expected concentrations, the LM3-Atrazine model 
was run for the period 1994-2005. Loads were 
estimated by repeating the 1994-1995 loading time- 
series over the 12-year period. The model was run 
using both the USGS-estimated loads from 1994- 
1995 and loads based upon long-term trends. Mid¬ 
lake concentrations were predicted to increase from 
the 1994 concentration of 37 ng/L to between 38 ng/L 
and 46 ng/L in 2005. 

Inflows and outflows of atrazine from the Lake 
Michigan system were tracked during the 1994-1995 
model simulations. Outflow through the Straits of 
Mackinac and decay losses were approximately 
equal and were the largest loss terms. Tributary 
inputs and atmospheric wet deposition were the 
largest sources of atrazine. Atmospheric exchange 
was minimal. 


Model results and measured data were compared to 
toxicological endpoints to examine possible 
ecological effects of atrazine concentrations in Lake 
Michigan. Most model forecast and data 
concentrations were below the toxicological 
endpoints of concern at the spatial scales used in 
these modeling analyses. 

5.2 LM3-Atrazine Recommendations 

Because of its high-resolution (5 km x 5 km model 
cells), LM3-Atrazine is useful to determine seasonal 
effects of loadings to various cells. Of particular 
interest may be the effects of high run-off in the 
spring after application to cells at the mouths of major 
tributaries. Within these cells, dramatic changes in 
atrazine concentrations may be noted over relatively 
short periods of time. Some of the highest 
concentrations in the lake would most likely be found 
at these sites. The lower-resolution models, 
MICHTOX and LM2-Toxic, have coarse 
segmentation and would not respond like the high- 
resolution model to these spring/early summer high 
loading events. In the coarse segmented model, the 
load is instantaneously dispersed uniformly into the 
much larger model segment volume receiving the 
river load. Hence, a concentration spike would be 
low compared to a high-resolution segment receiving 
the equivalent load. 

5.3 LM3-Atrazine Transport and Fate 
Modeling 

5.3.1 Purpose of High-Resolution Model 

Historically, water quality models for the Great Lakes 
have been developed using large spatial scales. The 
first eutrophication model for Lake Ontario (Thomann 
and Di Toro, 1975; Thomann et at., 1979) was 
configured with only two vertical segments 
(epilimnion and hypolimnion). Similar scale models 
were also developed for Lake Erie (Di Toro and 
Connolly, 1980), Lake Huron (Di Toro and Matystik, 

1980) , and Lake Michigan (Rodgers and Salisbury, 

1981) . Even a more recent model of Green Bay was 
developed on a relatively coarse-grid scale (DePinto 
et al., 1993). These models were capable of 
adequately simulating average water quality over 
large spatial segments and projecting future 
concentrations. However, they were not capable of 


108 




simulating spatial concentration gradients very well, 
if at all. Also, there have been questions on whether 
limnological processes could adequately be 
represented on such a large spatial scale, particularly 
sediment transport. During the design phase of the 
LMMBP, modelers were determined to construct a 
higher-resolution model to overcome these 
deficiencies. 

The LM3 level models include linked high-resolution 
hydrodynamic and water quality components. The 
hydrodynamic component of the models was 
developed by modelers at the NOAA’s Great Lakes 
Environmental Research Laboratory (GLERL). The 
transport framework was based on the U.S. Army 
Corps of Engineers’ CE-QUAL-ICM model (Cerco 
and Cole, 1994). The water quality components were 
developed at the USEPA’s LLRS. Completed water 
quality components included a eutrophication model 
(Pauer et a!., 2006), the atrazine model described 
below, and, subsequent to the LMMBP, an 
ecosystem model (Miller et a/., 2007). 

Although the LM3 level models have many scientific 
and technological advantages, there are major 
challenges. First, the LM3 models required a much 
greater degree of computer resources to develop and 
operate. Second, they required more computer 
programming support to develop completely new 
programs. Third, because there are over 40,000 
water segments for which concentrations are being 
simulated, there is much more computer output to 
manage and evaluate. This has presented disk 
storage issues and has required additional effort to 
develop computer programs to analyze and display 
model output. 

The following sections describe the LM3-Atrazine 
model, the assumptions used in developing the 
model, the loading data and lake concentrations used 
for model confirmation, and the results of model 
simulations for the 1994-1995 LMMBP period and for 
forecasts. 

5.3.2 Model Description and Framework 

LM3-Atrazine, as with most mass balance models, 
incorporates segment geometry, advective and 
dispersive transport, boundary concentrations for 
state variables, point and diffuse source loads, kinetic 


parameters, constants and time functions, and initial 
conditions. These input data, together with the 
general mass balance equations and the specific 
chemical kinetics equations, uniquely define a special 
set of water quality equations. These equations are 
numerically integrated as the simulation proceeds in 
time. At user-specified print intervals, values of 
selected state variables are saved for subsequent 
evaluation in visualization and statistical post¬ 
processor programs. 

In the Great Lakes environment, atrazine has the 
chemical properties of a mostly conservative 
substance. The important functions of the LM3- 
Atrazine model consist of hydrodynamic transport, 
external loads, atmospheric exchange, and a small 
first-order decay rate. 

This section contains a description of the 
hydrodynamic model, the kinetic processes of the 
atrazine model, and the spatial and temporal 
configuration of the atrazine model. 

5.3.2.1 POM Hydrodynamic Model 

The basis of the LM3 water quality model is water 
movement and material transport. Hydrodynamic 
simulations were conducted by Schwab and Beletsky 
(1998) who applied the POM. Portions of the 
following section are excerpted from their report. 
Subsequent to the preparation of the report, Schwab 
included annual average tributary flows and average 
Straits of Mackinac outflow in the final submission of 
model results to USEPA for use in mass balance 
models. In addition, computational modifications 
were made that eliminated a minor problem with 
water balance [for a technical discussion of the 
details, see Appendix A in Melendez et at. (2008)]. 
The primary goal was to provide three-dimensional 
fields of currents, temperature, and wind-wave 
characteristics for the study period (1994-1995) for 
direct input to the LM3 water quality model. The 
model was applied to Lake Michigan using a 5 km x 
5 km grid (Figure 5.1). The output of POM 
simulations was provided to the water quality 
modeling team at the USEPA/LLRS for further 
translation for the water quality models. 


109 










T 


Figure 5.1. Lake Michigan hydrodynamic model 
5 km x 5 km computational grid. 


During the development of the POM for Lake 
Michigan, the model was applied for two periods: 
1982-1983 and 1994-1995. The first period was 
chosen for model calibration because of an extensive 
set of observational data including surface 
temperature observations at two National Data Buoy 
Center (NDBC) weather buoys and current and 
temperature observations during June 1982-July 
1983 at several depths from 15 subsurface moorings. 

Results were output to files containing values for 
each of the 5 km x 5 km cells at specified time 
intervals. To compare model simulations with data, 
model results were averaged over various time 


periods depending on the data period. For example, 
the simulated temperature time-series for the 1982- 
1983 period are shown in Figure 5.2 and for the 
1994-1995 period in Figures 5.3a and 5.3b. 
Statistics of temperature field validation are 
presented in Table 5.1 for 1982-1983 and Table 5.2 
for 1994-1995. RMSD is the root mean square 
difference (error) between observed and computed 
temperatures. Maximum Error is the maximum 
temperature difference. Average is the arithmetic 
mean. The correlation coefficient provides a 
statistical indication of the strength of the linear 
relationship between computed and observed 
variables. 

The model was able to reproduce all of the basic 
features of the thermal structure of Lake Michigan 
during the 600 day period of study: spring thermal 
bar, full stratification, deepening of the thermocline 
during the fall cooling, and the overturn in the late fall 
(Figure 5.4). 

Another model validation was made by comparing 
observed temperature profiles acquired during the 
seven Great Lakes National Program Office 
(GLNPO) water quality surveys during 1994-1995 to 
simulated temperature profiles at 20 locations. 
Figure 5.5 depicts one of these locations, Station 
18M. In addition, the USGS conducted several near¬ 
shore transect surveys and compared simulated and 
observed temperatures. 

Schwab and Beletsky (1998) provided additional 
information on model development and validation. 
The basic conclusion was that, overall, the models 
simulated the large scale thermal structure, 
circulation, and waves quite realistically on the 5 km 
x 5 km grid. There were some qualifications, 
however. First, lack of an ice model will be a serious 
problem if the model is applied during a year with 
normal or severe ice conditions. It will cause both 
significant violations of the lake’s heat balance and 
errors in calculating transfer of momentum from air- 
to-water because of the difference in surface 
roughness of ice and water and momentum 
absorption by the ice. The 1994-1995 POM 
simulation assumed a constant uniform water 
temperature of 2°C for the period January 1 to 
March 31, 1994. Because no hydrodynamic data 


110 




















































































































































































































































































Surface Temperature at 45007 



Temperature at 15 m 



Temperature at 50 m 



Temperature at 148 m 



30 

T"* rr ^ r * yi-rt—pryr- 




o 

25 





<D 

20 





b 






"ro 

15 





aj 


. 




U- 

10 





<5 

l— 

5 






..I I ■ 1 ' ' ... ■ I I m il l l ii nm i i I i m t I I L 


100 200 300 400 500 600 
JULIAN DAYS, 1982-83 


Surface Temperature at 45002 



Temperature at 15 m 



Temperature at 50 m 



Temperature at 154 m 



Figure 5.2. Simulated temperature (black) compared to measured temperature (gray) at two buoys in 
Lake Michigan for 1982-1983 (Schwab and Beletsky, 1998). 


Ill 


























































Surface Temperature at 45007 



Temperature at 12 m 





100 200 300 400 500 GOO 
JULIAN DAYS, 1994-95 


Figure 5.3a. Time-series of simulated water temperature versus observed at 45007 for 1994-1995. Gray 
line is observation; black line is model simulation (Schwab and Beletsky, 1998). 


112 




















Surface Temperature at 45002 


100 200 300 400 500 600 
JULIAN DAYS. 1994-95 


Surface Temperature at 45010 

30 r~ r ~~— ' 



100 200 300 400 500 600 
JULIAN DAYS. 1994-95 


Figure 5.3b. Time-series of simulated surface water temperature versus observed at 45002 and 45010 
for 1994-1995. Gray line is observation; black is model simulation (Schwab and Beletsky, 1998). 


Table 5.1. 1982-1983 Hydrodynamic Model Evaluations for Surface Temperature at NDBC Buoys 
(45002 and 45007) and Subsurface Temperature at GLERL Current Meter Moorings (28 Instruments) 
(Schwab and Beletsky, 1998) 



RMSD 

Maximum 

Error 

Average 

Observed 

Average 

Computed 

Correlation 

Coefficient 

Surface 

1.2 

6.6 

12.1 

12.1 

0.99 

Subsurface 

Epilimnion 

2.5 

10.6 

7.1 

6.4 

0.87 

Hypolimnion 

0.7 

3.3 

4.2 

4.3 

0.78 


Table 5.2. 1994-1995 Hydrodynamic Model Evaluations for Surface Temperature at NDBC Buoys 
(45002, 45007, and 45010) and Subsurface Temperature at GLERL Current Meter Moorings (10 
Instruments) (Schwab and Beletsky, 1998) 


RMSD 

Maximum 

Error 

Average 

Observed 

Average 

Computed 

Correlation 

Coefficient 

Surface 

1.5 

6.1 

13.1 

13.3 

0.96 

Subsurface 






Epilimnion 

2.4 

9.2 

7.3 

7.7 

0.93 

Hypolimnion 

1.3 

5.2 

4.5 

5.3 

0.87 
























Figure 5.4. Simulated mean temperature (°C) profile for 1982-1983 (Schwab and Beletsky, 1998). 



Figure 5.5. Temporal evolution of simulated versus observed temperature profiles, Station 18M 
(Schwab and Beletsky, 1998). Black line is model simulation; gray line is observation. 


114 
































were available after December 21, 1995, the LM3- 
Atrazine model used the corresponding 1994 data for 
the last 10 days in 1995. 

Second, the model did not perform as well in the 
thermocline area as it did near the surface. The 
simulated thermocline was too diffuse. Although this 
problem might be overcome by development of a 
higher-resolution model, this problem is probably not 
significant for the mass balance study in comparison 
to other uncertainties with data and chemical and 
biological processes. 

Lastly, while the MICHTOX and LM2-Atrazine models 
have bidirectional flow through the Straits of 
Mackinac, the present configuration of LM3-Atrazine 
only uses a net, annual average outflow. In reality, 
there is a return flow to Lake Michigan at the Straits 
during stratification for a period of approximately 100 
days in the summer. However, to include this 
process within POM would have required significant 
additional resources including the running of a 
simultaneous Lake Huron hydrodynamic model. The 
absence of bi-directional flow at the Straits was not 
expected to have a significant impact on circulation 
predictions in the main portion of the lake. 

5.3.2.2 Model Framework 

The LM3-Atrazine model uses the same computer 
code and spatial resolution as other LM3 models 
(LM3-Eutro and LM3-Eco). Detailed documentation 
of the LM3 models has been provided by Melendez 
et al. (2008). The documentation provides a history 
of the models’ development and a complete 
description of the model framework, equations, and 
use. Documentation of the LM3-Eutro application is 
included in Pauer et al. (2006). Version 3.2.15 of the 
LM3 model code was used for the LM3-Atrazine 
model analyses. 

The transport model incorporated within the LM3 
framework was based on the ULTIMATE QUICKEST 
transport scheme, originally developed by Leonard 
(1991) and subsequently augmented for use with 
variable grid sizes by Chapman et al. (1997). The 
transport algorithm was coded in Fortran and 
previously applied to the Chesapeake Bay model 
(CE-QUAL-ICM) (Cerco and Cole, 1994, 1995). The 
transport model calculation performed numerical 
integration of spatially varying concentrations using 


quadratic interpolations of the concentration to infer 
its value at flow faces and analytic integration over 
space- and time-variables to account for changes in 
the concentration at the cell wall during the course of 
the time step. Further details of the dimensional 
derivation of the ULTIMATE QUICKEST transport 
method can be found in Melendez et al. (2008). 

Because atrazine is relatively stable in Lake 
Michigan, only a subset of the model’s kinetic 
processes were used: hydrodynamic transport, 
atmospheric exchange, and degradation. These 
processes, and the spatial and temporal resolution 
used in the simulations, are described below. 

5.3.2.2.1 Water Quality Processes 

The LM3 models are mass balance models based on 
the principle of conservation of mass. They use the 
same finite segment modeling approach used in the 
USEPA-supported WASP4 and the CE-QUAL-ICM 
modeling framework. The models describe where 
and how a mass of constituent is transported and 
transformed. The mass of a chemical or solid in 
each water segment is controlled by water movement 
between adjacent segments, solids and chemical 
dynamics within the system, internal and external 
loads, and boundary concentrations. 

For LM3-Atrazine, external loads, hydrodynamic 
outflow, and chemical transformation are the most 
significant processes affecting atrazine 
concentrations in Lake Michigan. Atmospheric 
exchange (volatilization and absorption at the 
water/air interface) was also included in the model 
kinetic process, although the mass involved is 
considerably smaller than that involved with outflow 
or chemical transformation. Atrazine does not 
partition onto solids. Thus the settling and sediment 
interaction portions of the LM3 water quality model 
were not utilized. 

Mass balance equations representing the above 
processes were used in the model to compute the 
change of mass of atrazine in each segment at a 
certain time. A general time-dependent finite 
differential equation in a given segment can be 
written to describe the change of mass for a state- 
variable at a certain time. The change in mass of 
atrazine in the LM3-Atrazine model for a given water 
column segment is described as: 


115 







= 2 Q t C, *1 R t (C r C,) 


+ w. + s . + S„ • 

J aw,] k,j 


The mass change due to kinetic transformation 
processes, S kj , is represented in the atrazine model 
by a single first-order decay rate. The decay 
(5.3.1) coefficient was determined during the long-term 
hindcast simulations using the MICHTOX and LM2- 
Atrazine models (Parts 3 and 4 of this report) and 
was set at 0.009 year' 1 (2.854 x 10' 10 s' 1 ). 


volume of segment j (L 3 ) 

concentration of water quality constituent in 
segment j (M/L 3 ) 

concentration of water quality constituent in 
segment I (M/L 3 ) 

concentration of water quality constituent at 
the interface between segment I and j 
(M/L 3 ) 

net flow across the interface between 
segment I and j (defined as positive when 
entering segment j and negative when 
leaving segment j) (L 3 /T) 

number of adjacent segments 

(EjjAj/AXjj), bulk dispersion/diffusion 
coefficient (L 3 /T) 

mixing (dispersion/diffusion) coefficient 
(L 2 /T) 

interfacial area between segment I and j 

(L 2 ) 

characteristic mixing length between 
segments I and j (L) 

external loading rate of segment j (M/T) 

mass change rate due to air-water 
exchange process between segment j and 
air directly above segment j (M/T) 

mass change rate due to sum of kinetic 
transformation processes within segment j 
(M/T), positive is source, negative is sink 


Gas exchange (volatilization and absorption) of 
atrazine between the lake and the atmosphere is a 
potential source or loss of atrazine to Lake Michigan. 
Computing the atrazine mass transfer across the 
water-air interface was necessary to satisfy the 
overall atrazine inventory and mass budget in the 
Lake Michigan system for the LMMBP period. The 
mass change rate term (S awJ ) for atrazine due to air- 
water exchange processes was calculated in 
Equation 5.3.2 as a product of the overall net mass 
exchange flux and surface area of the water 
segment j. 




—) * K 

H' 01 



(5.3.2) 


where 


k ol = the overall mass exchange rate coefficient 

(L/T) 

C dwj = dissolved atrazine concentration in water 

(M/L 3 ) 

C aj = atmospheric atrazine concentration over 
segment j (M/L 3 ) 

H’ = temperature-dependent Henry’s law 
constant (dimensionless) 

Aj = surface area of the water segment j (L 2 ) 


The overall mass exchange rate coefficient (k ol ) was 
calculated using the Whitman two-film theory 
formulation (Whitman, 1923) given as: 


k, k g *H' 


(5.3.3) 


L = length; M = mass; T = time. 







where 


k, 

k 9 


the liquid film mass transfer rate coefficient 

(L/T) 

the gas film mass transfer rate coefficient 
(L/T) 


equations derived from Scholtz et al. (1999) and 
Miller (1999). 


logH' 


\og(H Tlr!l )-AH H 


(VT-VTJ 

(2.303R) 


where 


(5.3.6) 


The parameters H’, k, and k g were calculated at 
every time step for each LM3 segment. The 
Wanninkhoff (1992) formulation for water mass 
transfer resistance and the Schwarzenbach 
(Schwarzenbach et al., 1993) formulation for gas 
mass transfer resistance were used for modeling the 
air-water exchange of atrazine in Lake Michigan. 
The Wanninkhoff equation for k„ with correction for 
atrazine molecular diffusivity in reference to carbon 
dioxide (C0 2 ) molecular diffusivity across the air- 
water interface, is given as: 


(5.3.4) 


k, = 0.45 * 


D 


w 


D, 


CO. 




H’ 


^Tref 

AH h 

R 

T 


temperature-dependent Henry’s law 
constant (dimensionless) 

Henry’s law constant at the reference 
temperature 

the enthalpy of phase change (kJ/mol) 

the ideal gas constant, 8.315 x 10' 3 
kJ/(mol)(°K) 

interfacial temperature (°K) 

reference temperature of 298.16 K (25° 

C) 


where 

D w = chemical molecular diffusivity in water 
(L 2 /T) 

D, CQ2 = C0 2 molecular diffusivity in water (L 2 /T) 

u 10 = wind velocity measured at 10 m above 

water surface (L/T) 

The Schwarzenbach formulation for k g with correction 
of atrazine molecular diffusivity in reference to water 
vapor molecular diffusivity across the air-water 
interface is given as: 

( D ) 067 

k g = (0.2*tv 10 + 0.3)* (5.3.5) 

{ u 9-H 2 o) 

where 

D a = chemical molecular diffusivity in air (L 2 /T) 

D g H2Q = water vapor molecular diffusivity in gas 
phase (L 2 /T) 

The atrazine model calculated a temperature- 
corrected dimensionless Henry’s law coefficient using 


The value for the dimensionless Henry’s law constant 
at 25°C was set to 8.1 x 10' 8 (U.S. Department of 
Agriculture, 2001). The enthalpy of phase change 
was set to 50 kJ/mol (Scholtz et al., 1999; Miller, 
1999). 

5.3.2.2.2 Spatial Resolution 

Developing the high-resolution grid for the LM3 
models required compromises between different 
spatial configurations and the difficulties in translating 
the 5 km x 5 km grid hydrodynamic output. The best 
approach was to develop the fine-grid model at the 
same 5 km scale as the POM (Figure 5.1). The high- 
resolution LM3 grid consisted of 2,318 
horizontal segments with 19 vertical “sigma” layers, 
resulting in a total of 44,042 water column cells. 

A linkage between POM and the LM3 model was 
developed by Chapman et al. (1997). The linkage 
mapped POM cell numbers with ULTIMATE 
QUICKEST flow face numbers and the relationship 
between horizontal and vertical components. LM3- 
Atrazine inputs included POM output for water 
temperature, horizontal and vertical dispersion, and 
horizontal and vertical currents for each segment in 
the water column. 


117 















5.3.2.2.3 Temporal Resolution 

The LM3-Atrazine model simulated the period from 
January 1, 1994 through December 31, 1995 for the 
LMMBP study period. To forecast the possible range 
of atrazine concentrations expected in Lake Michigan 
during the 2005 sampling surveys, the model was 
also run for the 12-year period January 1, 1994- 
December 31,2005. 

The LM3-Atrazine model was run using a variable 
time step based upon model stability. Over the 
course of the 1994-1995 simulation, the average 
value of the time step was approximately three hours. 
Output from the POM hydrodynamic model was 
averaged over three-hour intervals for input to the 
LM3-Atrazine model. LM3-Atrazine model results 
were output at a daily interval for two-year model 
runs. Atrazine almost behaves as a conservative 
constituent in Lake Michigan (has an extremely slow 
chemical transformation), and daily behavior 
provided sufficient resolution for interpretation of 
simulation results. Results from some of the 12-year 
forecast model runs were output at a six-day 
frequency to maintain reasonable output file sizes for 
long-term output animations. 

5.3.2.2.4 Model Assumptions 

The conceptualization of processes in the LM3- 
Atrazine model was based upon literature review 
(Part 1, Chapter 2) and previous LLRS atrazine 
modeling efforts (Part 3 and Part 4). Atrazine 
essentially behaves as a conservative substance in 
Lake Michigan. Previous LLRS modeling 
demonstrated that external loading and outflow from 
the Straits of Mackinac were the most important 
processes effecting atrazine concentrations in the 
lake (Part 3; Part 4; Rygwelski et al., 1999). 
Although it occurs slowly, degradation of the 
chemical is also an important process because of the 
slow rate of export. Exchange between the water 
surface and atmosphere was modeled, although it 
only had a small effect on lake concentrations. 

Atmospheric loads were assumed to be primarily 
through wet deposition. Dry deposition was not 
found to be significant based upon LMMBP sampling 
reports (Brent et al., 2001; Miller, 1999). Sections 
1.3.2.2.2 and 1.3.2.2.3 of this report summarize the 
atmospheric deposition sampling. Later papers have 


suggested that dry deposition may be significant 
(Miller et al., 2000) but only provided a range of 
possible loads and no spatial or temporal resolution 
consistent with the LM3 models. The range of 
possible dry deposition loads was taken into 
consideration when estimating loads for long-term 
forecasts. 

Atrazine is primarily found in the dissolved state in 
Lake Michigan, and sediment interactions with 
atrazine are minor (Part 1, Chapter 2; Rygwelski et 
al., 1999). Sediment processes were assumed to be 
negligible and were not included in the LM3-Atrazine 
model kinetics. 

5.3.3 Description of Data Used 

The data used for the LM3-Atrazine modeling was 
collected during the 1994-1995 LMMBP studies. The 
data were reviewed in Brent et al. (2001) and 
summarized in Part 1, Chapter 3 and Part 2 of this 
report. 

5.3.3.1 Field Data 

Model simulation results were compared to data 
collected during the LMMBP field surveys. Lake 
water samples were collected for atrazine analysis 
during six cruises from April 1994 through April 1995. 
Data from mid-lake stations were selected for 
comparison purposes because these stations were 
sampled during most cruises. While data were also 
collected from near-shore stations, these stations 
were not routinely sampled. 

5.3.3.2 Initial and Boundary Conditions 

The Lake Michigan atrazine model initial 
concentrations were estimated based upon the 
LMMBP field survey data. A uniform concentration of 
37 ng/L was set for all main lake and northern Green 
Bay model cell initial concentrations based upon the 
average concentrations measured during the spring 

1994 sampling cruise. Southern Green Bay cells 
nearest to the Fox River were assigned an initial 
concentration of 50 ng/L based upon the limited 
Green Bay sampling from the fall 1994 and spring 

1995 cruises. 

While the MICHTOX and LM2-Atrazine models have 
bidirectional flow through the Straits of Mackinac, the 


118 



present configuration of LM3-Atrazine only uses a net 
outflow. For this assumed configuration, Lake Huron 
boundary conditions are not necessary because 
there is no flow to Lake Michigan at the Straits of 
Mackinac. 

Atmospheric atrazine vapor samples were collected 
from March 1994 through October 1995. 
Atmospheric sampling did not detect vapor phase 
atrazine concentrations in 86% of the samples (Brent 
et al., 2001). For modeling purposes, the 
atmospheric concentration for all locations and times 
was set to a single value equal to one-half the 
average method detection limit (MDL) of the 
samples, 4.63 pg/m 3 (Miller 1999). 

5.3.3.3 Loadings 

5.3.3.3.1 Tributary 

Watershed atrazine loadings to Lake Michigan were 
estimated by Hall and Robertson (1998). Loads were 
calculated for 11 tributaries that were sampled as 
part of the LMMBP field program and for 18 
unmonitored watersheds (Figure 5.6). For the 
monitored tributaries, event and base flow samples 
were collected from April 1995 through October 
1995. The Stratified Beale Ratio Estimator (SBRE) 
was used to calculate loads for 1995 with these 
sample data and the USGS flow data. Because 
tributary samples were not collected in 1994, loads 
for 1994 were estimated using USGS regression 
methods and the 1995 data (Hall and Robertson, 
1998). Loads for the unmonitored watersheds were 
estimated using load to watershed area ratios from 
monitored watersheds with similar soils and land 
uses. Part 2, Chapter 2 discusses the tributary loads 
in more depth. 

The USGS estimated loads for 1994-1995 were 
substantially smaller than what would have been 
expected based upon long-term loading patterns. 
Rygwelski et al. (1999) reviewed previous studies 
and found that, for soils similar to those in the corn- 
producing watersheds of Lake Michigan, 0.6% of the 
atrazine applied to the watershed reached Lake 
Michigan. This amount is also referred to as the 
Watershed Export Percentage (WEP). Rygwelski et 
al. (1999) also conducted long-term hindcast atrazine 
modeling that confirmed the appropriateness of the 
0.6% WEP (see Parts 3 and 4). The 1994-1995 



Manistique 


Menominee 


Muskegon 


ygan 


) Pere ( 
arquette, 


Milwaukee 


Grand 


alamazo 


St. 

Joseph 


Grand 


Lake Michigan 
watersheds 


monitored 

’tributary 


basins 


unmonitored 

tributary 

basins 


sampling 

locations 


Figure 5.6. Watershed and mid-lake sampling 
stations for the LMMBP study. 


USGS estimated tributary loads were only 30% of the 
load estimated using the long-term WEP and 1994- 
1995 atrazine application data for the Lake Michigan 
watershed. Using the USGS-estimated loads for 
1995, a WEP of 0.12% was calculated. Using this 
WEP derived from the USGS load and no atrazine 
decay in a MICHTOX hindcast, the model-predicted 
less than one-half of the measured concentration in 
the lake as observed in the mid-1990s. 

The 1994-1995 loads may have been substantially 
lower because of a number of possible factors: loads 
were lower than normal due to the WEP possibly 
decreasing over time because of improved 
agricultural management practices, significant peaks 
in tributary loads may have been missed because the 
weekly storm event sampling was discontinued too 
early, or atmospheric dry deposition may be higher 
than expected. It is known that dry years can 
depress atrazine watershed loadings. However, 


119 












precipitation to the lake was near long-term averages 
(see Sections 1.3.2.2.3, 1.4.5.1, 1.4.5.2, and 2.2.1.2 
for information on rainfall and impact on WEPs). 
Other potential meteorological forcing functions were 
also near average conditions during 1994-1995 (see 
Part 1, Chapter 4). Therefore, weather conditions 
are an unlikely cause of the low USGS export 
estimates. 

The USGS loadings were based on an average of 14 
atrazine samples per year per tributary (range: 
seven to 20). When compared to other similar 
atrazine load estimation studies (Schottler et al., 
1994; Richards et al., 1996; Capel and Larson, 
2001) this represents a very low number of samples 
collected and thus could have contributed to 
underestimation of loads. A study by Leu et al. 
(2004) found that a single run-off event that occurred 
on day 23 after application of atrazine exported 70% 
of the total cumulative load measured during a one- 
to 67-day period after application. On a fine-loamy 
field in Ohio, a rainstorm occurred just two days after 
atrazine application to a no-till field. That rain event 
accounted for only 3% of the yearly rainfall and 6% of 
the yearly run-off; yet it produced 78% of the yearly 
atrazine loss (Shipitalo and Owens, 2003). The 
Shipitalo and Owens’ study also concluded that the 
timing of rainfall and run-off relative to atrazine 
application can have a much greater effect on yearly 
losses than agronomic management practices (till 
versus no-till). So, a lack of adequate sampling 
during an event shortly after atrazine application 
could cause significant underestimation of the total 
annual loading from a watershed. Also, Schottler et 
al. (1994) and Williams et al. (1995) have noted that 
the spring atrazine concentration often peaks in 
streams just before the maximum flows are 
achieved. One possible theory suggests that a 
fraction of atrazine on the soil immediately following 
application is readily available for transport by run-off 
during a precipitation event. However, later in the 
season, the peak concentration may actually lag the 
peak flow suggesting that export from the fields is 
associated with water that has infiltrated the soil and 
carried via shallow saturated zones or surface 
drainage tile networks to receiving tributaries. High 
frequency sampling just before, during, and after a 
flow event are important in order to fully capture 
atrazine loading events. 


To evaluate the possible range of loads occurring in 
the Lake Michigan system, additional model runs 
were conducted with annual loads set equal to those 
expected based upon the long-term WEP of 0.6%. 
The USGS loads were adjusted by multiplying loads 
from each tributary during a 90-day period from April 
15 to July 13 by a factor that resulted in the loads for 
that tributary being equal to the expected WEP- 
based load. Only the spring period was multiplied 
since this is the period when the majority of atrazine 
loads enter the lake and this is the period when 
tributary and atmospheric loads have the largest 
uncertainty. Load multiplication factors were 
calculated as the multiplier for the specified time 
period loads that set the total 1994 and 1995 USGS- 
calculated loads for each tributary equal to the 
combined WEP loads for both years for that tributary. 
Computer code in the LM3-Atrazine model conducted 
the multiplication during the model simulation by 
reading inputs for the scaling time periods for each 
year and the multiplication factors for each tributary. 
WEP-based loads and USGS-estimated loads are 
listed in Section 2.2.5. Figure 5.7 displays the USGS 
and WEP-based loading time-series for the three 
largest tributary loadings of atrazine to Lake 
Michigan. 

A loading series for the 12-year model runs was 
developed by repeating the loads for the 1994-1995 
period six times, using USGS-calculated or WEP- 
adjusted loads as appropriate. The LM3-Atrazine 
model did this automatically by looping over the two- 
year loading input deck and applying the load 
multiplier factors as needed. 

In Figures 5.7-5.10 and for the remainder of this part, 
the “long-term WEP loads” in the legends refer to 
USGS loads that were adjusted as described in the 
preceding paragraphs and “USGS estimated loads” 
refer to the loads as received from the USGS. 

5.3.3.3.2 Atmospheric 

Atmospheric deposition samples were collected from 
March 1994 through October 1995. Wet deposition 
was the dominant atmospheric source of atrazine to 
Lake Michigan. The monthly average 1994 and 1995 
wet deposition loading time-series data were 
provided by Hornbuckle (University of Iowa, personal 
communication, 2002; Miller et al., 2000). Dry 
deposition was not included in the atmospheric loads. 


120 



Ol 

■O 

n 

o 

4) 

C 

N 

ra 



o 

-X 

■c 

re 

o 

0) 

c 

'n 

re 


D) 

■O 

re 

O 

0) 

c 

N 

re 


Grand River j 

t 


t 


J 1 1 

a 


I 


Jan 

1994 


July 

1994 


Jan 

1995 


July 

1995 


Jan 

1996 


St. Joseph River 


I 



~ long-term WEP loads 
— USGS estimated loads 



Jan 

1994 


July 

1994 



Grand River 


St. Joseph River 


Figure 5.7. Atrazine loads for Lake Michigan tributaries, 1994-1995. 


Estimates from the LMMBP study found it to be small 
(208 kg/year) (Miller, 1999). However, later 
estimates placed the range of dry deposition loads 
from 230-1000 kg/year (Miller et al., 2000). For 
additional discussion on atrazine in the particulate 
fraction, see Part 1, Chapter 3. 

5.3.4 Description of Model Simulations 
and Results 

Model simulations were conducted for different load 
scenarios and time periods. Simulations were 
conducted with tributary loads based upon the 1994- 
1995 USGS estimates and with loads based upon 
the long-term WEP loads. Atmospheric loads were 
kept constant for all model runs. Time periods 
modeled included the two-year period of the LMMBP 
(1994-1995) and the 12-year period from 1994 to 
2005. 

For the 1994-1995 simulation using USGS tributary 
loads, mid-lake model results generally matched field 
data (Figure 5.8). Mid-lake model predictions varied 
little over the course of a year. The 1994-1995 
simulation using the long-term WEP loads (Figure 


5.8) was run to represent long-term trends of 
loadings. These higher loads resulted in greater 
seasonal increases in mid-lake concentrations 
compared to the USGS-estimated loads, but annual 
variation was still relatively small. 

The high-resolution LM3 model was especially useful 
in demonstrating near-shore impacts resulting from 
various watershed loads. Higher concentrations 
were predicted in the areas near the mouths of major 
tributaries during the spring and early summer period 
when loadings were highest (Figure 5.9). The high- 
resolution model was also useful for demonstrating 
the impact of different loading scenarios on small 
spatial areas. Model results from the 5 km x 5 km 
model segments directly off the mouths of the major 
tributaries showed the largest variations of atrazine 
in the lake (Figure 5.10). Predicted concentrations 
directly off the mouth of the St. Joseph River ranged 
as high as 102 ng/L using the USGS-estimated loads 
and as high as 350 ng/L using the long-term WEP- 
based loads model runs. 















































50 


40 


o> 30 

_C_ 

01 

c 

n 20 

1 0 


Station 27M 


A M 

A 



- ¥ 

i - 



10 


Jan 

1994 


50 


o>_ 

= j 

TO O) 
■- C 


40 


30 


long term WE P loads 
““ USGS estimated loads 
a field data (0-10 m) 


20 


10 ■ 


i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i- 


Station 47M 


0 -I—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—I 


July 

1994 


Jan 

1995 


July 

1995 


Jan Jan-1994 July-1994 

1996 


Jan 

1995 


July 

1995 


Jan 

1996 


50 


40 


30 

v 

I 20 


10 


Station 18M 


A 

TT^r 




0 f ■ i i 


-i—i—i—i—i—i—i—i—!—i—i—i—i—i—i—i—r 


50 


40 


<? 30 
c 

a 20 


10 


Stations 40M/41 




0 i i i i i i i i i i i i i i i i i i i i i i i r~ 


Jan 

1994 


July 

1994 


Jan 

1995 


July 

1995 


Jan Jan 

1996 1994 


July 

1994 


Jan 

1995 


July 

1995 


Jan 

1996 


Figure 5.8. Comparison of field data to predicted mid-lake surface concentrations for the 1994-1995 
model simulation and two loading conditions. Station locations are shown in Figure 5.6. 

May 29, 1995 


600 

42 


Manistique 

i <* J 


41 


40 


1 


39 


38 



Manitowac o 
Sheboygan * 


Milwaukee <• 
Root o 


A.>Pere Marquette 


Muskegon 
Grand 

Kalamazoo 

St. Joseph 


Calumet 0 


37 
Atrazine 
(ng/L) 

Figure 5.9. Model simulation results of surface concentrations for May 29,1995 using long-term WEP- 
based loads. Selected tributary input locations are labeled. 


122 







































Figure 5.10. Comparison of near-shore surface cell model results for the 1994-1995 model simulation 
and two loading conditions. 


While the model is useful for demonstrating near¬ 
shore impacts, it does not simulate concentrations in 
a river plume entering the lake or in the tributary 
itself. During model simulations, at each time step, 
any tributary load is immediately mixed throughout 
the 5 km by 5 km model cell near the tributary mouth, 
and thus predicted near-shore concentrations are a 
function of the size of the model cells and not 
representative of concentrations in river plumes in 
the lake. Furthermore, the LM3-Atrazine model was 
not designed to make predictions of atrazine 
concentrations in any of the tributaries. 

The model results reasonably fit the available data, 
and no adjustments were made to the initial model 
kinetic parameters. A better model fit to data 
probably could have been obtained by using different 
initial concentrations in different regions of the main 
lake rather than a uniform, lake-wide initial 
concentration. However, since the January 1994 
initial concentrations were estimated from spring 
1994 data, it was believed that the sampling data 
were not sufficient to justify that change. 

A longer-term data set would be required to fully 
calibrate the LM3-Atrazine model. However, 
confidence in model predictions was provided in two 


ways. First, atrazine in Lake Michigan acts as a 
mostly conservative chemical, and the model was 
previously calibrated to the conservative chemical 
chloride in Lake Michigan (Richardson et al., 2001). 
Thus there is confidence that the transport of 
substances, one of the primary loss processes of 
atrazine, was being correctly simulated. The high- 
resolution transport is the primary difference between 
LM3-Atrazine and the MICHTOX and LM2-Atrazine 
models. Second, an acceptable simulation of 
atrazine concentrations was obtained using model 
parameters derived from literature and previous 
modeling studies, providing confidence in the kinetic 
formulations and kinetic parameterization of the 
model (Rygwelski et al., 1999; Part 3; Part 4). 

As part of the 2005 Lake Michigan sampling effort, 
the USEPA collected atrazine samples at multiple 
stations during multiple cruises. These data were not 
available at the time of this report, but, when 
available, they will provide a comparison of atrazine 
concentrations to those measured in 1994-1995 and 
an estimate of the change in atrazine mass in the 
system over that time period. To estimate potential 
changes in Lake Michigan atrazine concentrations 
during the 1994-2005 time period, additional LM3- 
Atrazine model simulations were conducted. Two 




123 






































model runs were conducted: one with the 1994-1995 
USGS-estimated loads and one with the WEP-based 
loads. The 12-year loading time-series for these runs 
was developed by repeating the appropriate 1994- 
1995 loading time-series and hydrodynamics six 
times. Assuming that atrazine usage in the 
watershed did not change significantly from that 
during 1994-1995, results from these model 
simulations will likely bracket the concentrations from 
the 2005 sampling period. Predicted mid-lake 
concentrations for 2005 ranged from 38 ng/L for the 
USGS tributary loading time-series to 46 ng/L for the 
WEP-based tributary loading time-series (Figure 
5.11). Tributary loads were not sampled during the 
2005 surveys, but by calculating the change in the in¬ 
lake atrazine inventory and comparing it to the load 
scenarios used for model runs, the actual magnitude 
of present watershed loads will be able to be 
estimated. 

5.3.4.1 Mass Budgets 

Inflows and outflows of atrazine to the Lake Michigan 
system were tabulated during the 1994-1995 model 
simulation runs (Table 5.3, Figure 5.12). For the 
model run using the USGS-estimated tributary loads 
for 1994-1995, the largest source of atrazine was wet 
deposition from the atmosphere. The percentage of 
loads from tributary sources was only slightly less. 
For the model run using the long-term WEP-based 
1994-1995 tributary loads, tributary loads dominated 
and were almost three times higher than wet 
deposition. Absorption from the atmosphere 
(volatilization in) was minimal for both cases. 

Losses from the Lake Michigan system were similar 
for both loading scenarios. The largest losses of 
atrazine were from decay and outflow through the 
Straits of Mackinac, though the mass lost through 
these processes is relatively small compared to the 
total atrazine inventory in the lake. Outflow through 
the Chicago Ship and Sanitary Canal was a small 
percentage of total mass lost, and volatilization from 
water to the air was negligible. 

The annual net gain of atrazine to the system for the 
model run using the USGS-estimated loads was 380 
kg/year, or 11 % of the measured 1994-1995 loads to 
the system. Forthe model run using long-term WEP- 
based load estimates the net gain increased to 3,842 
kg/year, equal to 55% of incoming sources. 


5.3.4.2 Selected Model Versus Observation 
Statistics 

The variability in the field data made any comparison 
with model results difficult. There was as much 
variation between atrazine field duplicate samples as 
there was seasonal variation predicted by the model. 
Fifty-seven field duplicate and two field triplicate 
samples were collected as part of the LMMBP 
atrazine sampling. The median absolute difference 
between field duplicate samples was 1.8 ng/L, with 
the average relative percent difference (RPD) equal 
to 6%. Maximum seasonal variation in model results 
from representative mid-lake stations was 1.5 ng/L 
for the model run using USGS-estimated loads and 
6.6 ng/L for the model run using WEP-based loads. 

There were also no significant spatial or temporal 
trends in the Lake Michigan data (Brent et at., 2001) 
that would have assisted in evaluating model 
prediction capabilities. This may have been due to 
an actual lack of trends or because there was no 
near-shore sampling during the late spring and early 
summer period when the lake concentrations were 
predicted to be most affected by seasonal 
atmospheric and tributary loadings. 

5.3.4.3 Comparison to Toxicological Endpoints 

Model simulation and forecast results were plotted 
with measured data against toxicological endpoints to 
examine potential ecological effects of predicted 
atrazine concentrations in Lake Michigan (Figure 
5.13). Most forecast and data concentrations were 
below the selected toxicological endpoints of concern 
at the spatial scale used in these modeling analyses. 

The toxicological endpoints selected for Figure 5.13 
were developed as part of a review of toxicity studies 
used for determining the eligibility of atrazine for 
reregistration as an herbicide (U.S. Environmental 
Protection Agency, 2003a). Endpoints for important 
ecological components of the Lake Michigan system 
included fish, zooplankton, other invertebrates, and 
phytoplankton. Mortality endpoints correspond to 
acute, or short-term, toxicity studies. Growth or 
population reduction endpoints correspond to 
chronic, or long-term, toxicity studies. 


124 




60 


50 

40 

30 

20 

10 

0 



Station 27M 


La3 


Jan 

1994 


—i- 

Jan 

1997 


-1- 

Jan 

2000 


—i- 

Jan 

2003 


60 

50 

40 


o> 

c_ 

o 30 
c 

N 

ID 

i 20 - 

ID 


10 

0 


Station 47M 


Jan 

2006 


Jan 

1994 


-r- 

Jan 

1997 


Jan 

2000 


Jan 

2003 


Jan 

2006 


60- 

50' 

40 

30 

20 

10 

0 


Station 18M 


long term WEP loads 
USGS estimated loads 


-— 


60 
50 
5" 40 

O) 

c 

£ 30 


~ 20 

rtj 


10 

0 


Stations 40M/41 


Jan 

1994 


Jan 

1997 


Jan 

2000 


Jan 

2003 


Jan 

2006 


Jan 

1994 


Jan 

1997 


Jan 

2000 


Jan 

2003 


Jan 

2006 


Figure 5.11. Mid-lake surface concentration model results for the 1994-2005 model simulation and two 
loading conditions. 


Table 5.3. Mass Budget Average Annual Results for 1994-1995 Model Simulations. All are in kg/year. 



USGS Loads 

Long-Term WEP-Loads 

Mass Change 

380 


3842 


Inflows 





Loads 

3362 

98% 

6870 

99% 

Tributary 

1578 

(46%) 

5086 

(73%) 

Wet Deposition 

1784 

(52%) 

1784 

(26%) 

Volatilization In 

58 

2% 

58 

1% 

Outflows 





Decay 

1615 

53% 

1647 

53% 

Total Outflow 

1412 

46% 

1425 

46% 

Chicago Outflow 

110 

(4%) 

112 

(4%) 

Mackinac Outflow 

1302 

(43%) 

1313 

(43%) 

Volatilization Out 

13 

0% 

14 

0% 

Totals 





Net Volatilization 

-45 


-44 


Total In 

3420 


6928 


Total Out 

3040 


3086 


Average Mass Inventory (kg) 

179,459 


182,979 



125 



















































1578 

and unmonitored 
tributary loading 
(Lake Michigan watershed) 


wet 

deposition 
1784 


volatilization i 
out* 
13 

volatilization 
in 
58 


decay 

1615 


Chicago 

River 

outflow 

110 


Mackinac 

outflow 

1302 


USGS loads 
Atrazine Inventory 

water column = 179,459 kg 
mass change = + 380 kg/yr 



5086 

and unmonitored 
tributary loading 
(Lake Michigan watershed) 


volatilization 

out 

14 

volatilization 
in 
58 


wet 

deposition 
1784 


decay 

1647 


Mackinac 

outflow 

1313 


Chicago 
River 1 
outflow 
112 


Long-term WEP loads 
Atrazine Inventory 

water column = 182,979 kg 
mass change =+ 3842 kg/yr 


Figure 5.12. Mass budget average annual results for the 1994-1995 model simulations. All mass 
flow rates are in kg/yr. 


126 





















10,000 ^=r 


• - fish mortality (5300) 


1,000 — 


■* - draft acute toxicity criteria CMC (1500) 

- invertebrate mortality (720) 


O 


measured data 



• endpoint / criteria 


D1 


c 

o 

» 


c 

0 ) 

o 

c 

o 

O 

CD 

C 

'N 

ro 




invertebrate population reduction (62) 
fish population reduction (62) 
phytoplankton acute toxicity (32) 


10 — 


• -zooplankton population reduction (10) 




human drinking water MCL (3) 

maximum measured 1995 tributary concentration 
in St. Joseph River (2.7) 

phytoplankton primary production reduction (2.3) 


— 


highest Lake Michigan predicted concentration, 

— 


USGS 1994*1995 loads (0.10) - St. Joseph River mouth 

0.1 -= 

• 

• ^ 

Lake Michigan 2263 MICHTOX forecast concentration (0.066) 

— 

■ — 

o 

' ' ' - -' • - 

Lake Michigan 2005 LM3-Atrazine forecast range (0.038 - 0.046) 

— 


Lake Michigan 1994 average concentration (0.037) 


0.01 


Figure 5.13. Comparison of model predictions, measured data, and selected toxicological 
endpoints. 


Regulatory endpoints were also included in Figure 
5.13. These endpoints included proposed criteria for 
environmental protection and established limits for 
human health protection. Water quality criteria for 
the protection of aquatic ecosystems have been 
proposed for atrazine (U.S. Environmental Protection 
Agency, 2003b) but were not finalized at the time of 
this report. The draft acute toxicity Criterion 
Maximum Concentration (CMC) was included in 
Figure 5.13. While a draft chronic criteria was also 
published, it was not included in the figure because 
it was not based upon a single concentration. Ihe 
draft chronic criteria were based upon modeling 
ecological community changes in aquatic plants 
using both exposure concentration and duration. The 
human drinking water Maximum Contaminant Limit 
(MCL) is also included in the graph. 

Measured atrazine data collected during the LMMBP 
were below endpoints of toxicological concern except 
for one tributary sample from the St. Joseph River in 
May 1995. This measurement, 2.7 pg/L, exceeded 


the endpoint of 2.3 pg/L at which reductions in 
primary production of phytoplankton were estimated 
to occur. The St. Joseph River sample was also 
close to the human drinking water MCL. Detailed 
information on determining compliance with the MCL 
for atrazine can be found in 40 CFR 141.24(h). The 
second highest measured tributary concentration, 
0.55 pg/L, was a sample from the Grand River in May 
1996 and was below all selected toxicological 
endpoints. The 1994 Lake Michigan annual average 
atrazine concentration of 0.037 pg/L was well below 
the selected toxicological endpoints. 

Model forecasts were below all selected endpoints. 
The MICHTOX long-term steady-state forecast 
concentration of 0.066 pg/L was well below 
toxicological endpoints. The LM3-Atrazine 12-year 
(2005) forecast lake-wide concentration range was 
lower than the MICHTOX steady-state forecast 
concentration. The highest simulated single model 
cell concentration from the high-resolution LM3- 
Atrazine model was also below selected endpoints. 


127 
























The highest simulated concentration, using the 
USGS loading time-series, was 0.10 pg/L near the 
mouth of the St. Joseph River. It must be 
remembered that this concentration represents an 
average prediction from a volume representing a 5 
km by 5 km area of the lake which provides 
significant dilution to tributary event loads. The 
WEP-based loading time-series was not used in this 
analysis because the distribution of the long-term 
annual loads among seasons and short-term loading 
events was somewhat subjective. Thus, presenting 
a concentration prediction based upon this loading 
time-series at a single location and point in time 
would have a large amount of uncertainty. 

5.3.5 Model Uncertainty 

While the LM3-Atrazine could not be fully calibrated 
because of insufficient data, the basis upon which the 
model was developed provided confidence that 
model results were reasonable. The hydrodynamic 
model was successfully compared to two separate 
datasets (Schwab and Beletsky, 1998) and model 
transport of a conservative substance, chloride, was 
also calibrated (Richardson et al., 2001). The only 
additions to the chloride model for the LM3-Atrazine 
model were volatilization and kinetic decay terms. 
Volatilization was a minor effect on the fate of 
atrazine in the lake, and the decay term was based 
upon long-term hindcast calibrations with the 
MICHTOX and LM2-Atrazine models. Furthermore, 
the model provided reasonable fits to data without 
changing model kinetic parameters from the initial 
values based upon literature studies and previous 
atrazine model calibration studies. There may be 
some uncertainty about the decay term because the 
LM2-Atrazine model used to calibrate the term 
incorporated bi-directional flow at the Straits of 
Mackinacoutflowwhilethe LM3-Atrazinemodel used 
a net outflow from the Straits to Lake Huron. 
However, this would only have had a minor effect on 
the atrazine mass in the lake for the time periods 
modeled with the LM3-Atrazine model. 

There was probably more uncertainty from the 
loading data used in the model and the field data 
than from the model kinetic processes. The 
estimated 1994-1995 tributary loads were 
significantly less than those expected based upon 
previous long-term modeling studies, and it was not 
known if 1995 was a year of low atrazine loading, if 


storm events were missed during the tributary 
sampling, or if there were additional significant 
sources of loads such as dry deposition that were not 
measured. 

References 

Brent, R.N., J. Schofield, and K. Miller. 2001. 
Results of the Lake Michigan Mass Balance 
Study: Atrazine Data Report. U.S. Environmental 
Protection Agency, Great Lakes National 
Program Office, Chicago, Illinois. 
EPA/905/R-01/010, 92 pp. 

Capel, P.D. and S.J. Larson. 2001. Effect of Scale 
on the Behavior of Atrazine in Surface Waters. 
Environ. Sci. Technol., 35(4):648:657. 

Cerco, C. and T. Cole. 1994. Three-Dimensional 
Eutrophication Model of Chesapeake Bay. U.S. 
Army Corps of Engineers, U.S. Army Engineer 
Waterways Experiment Station, Vicksburg, 
Mississippi. Technical Report EL-94-4, 658 pp. 

Cerco, C. and T. Cole. 1995. User’s Guide to the 
CE-QUAL-ICM Three-Dimensional 
Eutrophication Model. U.S. Army Corps of 
Engineers, U.S. Army Engineer Waterways 
Experiment Station, Vicksburg, Mississippi. 
Technical Report EL-95-15, 2,420 pp. 

Chapman, R.S., T.M. Cole, and T.K. Gerald. 1997. 
Development of HydrodynamicA/Vater Quality 
(POM-IPXMT) Linkage for the Lake Michigan 
Mass Balance Project. Final Report. U.S. 
Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory-Duluth, Large Lakes 
Research Station, Grosse lie, Michigan. 63 pp. 

DePinto, J.V., R. Raghunathan, P. Sierzenga, X. 
Zhang, V.J. Bierman, Jr., P.W. Rodgers, and T.C. 
Young. 1993. Recalibration of GBTOX: An 
Integrated Exposure Model for Toxic Chemicals 
in Green Bay, Lake Michigan. Final Report. U.S. 
Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory-Duluth, Large Lakes 
Research Station, Grosse lie, Michigan. 132 pp. 


128 



Di Toro, D.M. and J.P. Connolly. 1980. 
Mathematical Models of Water Quality in Large 
Lakes, Part 2: Lake Erie. U.S. Environmental 
Protection Agency, Office of Research and 
Development, Environmental Research 
Laboratory-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. 
EPA/600/3-80/065, 97 pp. 

Di Toro, D.M. and W.F. Matystik, Jr. 1980. 
Mathematical Models of Water Quality in Large 
Lakes, Part 1: Lake Huron and Saginaw Bay. 
U.S. Environmental Protection Agency, Office of 
Research and Development, Environmental 
Research Laboratory-Duluth, Large Lakes 
Research Station, Grosse lie, Michigan. 
EPA/600/3-80/056, 180 pp. 

Hall, D. and D. Robertson. 1998. Estimation of 
Contaminant Loading from Monitored and 
Unmonitored Tributaries to Lake Michigan for the 
USEPA Lake Michigan Mass Balance Study. 
Quality Systems and Implementation Plan. 
Submitted October23,1998. U.S. Environmental 
Protection Agency, Great Lakes National 
Program Office, Chicago, Illinois. 19 pp. 

Leonard, B. 1991. The ULTIMATE Conservative 
Difference Scheme Applied to Unsteady One- 
Dimensional Advection. Comp. Methods Appl. 
Meehan. Engin., 88(1 ):17-74. 

Leu, C., H. Singer, C. Stamm, S.R. Muller, and R.P. 
Schwarzenbach. 2004. Simultaneous 
Assessment of Sources, Processes, and Factors 
Influencing Herbicide Losses to Surface Waters 
in a Small Agricultural Catchment. Environ. Sci. 
Technol., 38(14):3827-3834. 

Melendez, W., M. Settles, J. Pauer. 2008. LM3: A 
High-Resolution Lake Michigan Mass Balance 
Water Quality Model. U.S. Environmental 
Protection Agency, Office of Research and 
Development, National Health and Environmental 
Effects Research Laboratory, Mid-Continent 
Ecology Division-Duluth, Large Lakes Research 
Station, Grosse lie, Michigan. 285 pp. 


Miller, D.H., R.G. Kreis, Jr., W. Huang, and X. Xia. 
2007. Application of an Ecosystem Modeling 
Approach for Investigating Population Dynamics 
of the Invasive Species Bythotrephes longimanus 
in Lake Michigan. International Association for 
Great Lakes Research Annual Meeting, State 
College, Pennsylvania, May 28-June 1,2007. 

Miller, S.M. 1999. Spatial and Temporal Variability 
of Organic and Nutrient Compounds in 
Atmospheric Media Collected During the Lake 
Michigan Mass Balance Study. M.S. Thesis, 
Department of Civil, Structural, and 
Environmental Engineering, State University of 
New York, Buffalo, New York. 181 pp. 

Miller, S.M., C.W. Sweet, J.V. DePinto, and K.C. 
Hornbuckle. 2000. Atrazine and Nutrients in 
Precipitation: Results from the Lake Michigan 
Mass Balance Study. Environ. Sci. Technol., 
34(1 ):55-61. 

Pauer, J.J., K.W. Taunt, and W. Melendez. 2006. 
LM3-Eutro. In: R. Rossmann (Ed.), Results of 
the Lake Michigan Mass Balance Project: 
Polychlorinated Biphenyls Modeling Report, pp. 
120-182. U.S. Environmental Protection Agency, 
Office of Research and Development, National 
Health and Environmental Effects Research 
Laboratory, Mid-Continent Ecology Division- 
Duluth, Large Lakes Research Station, Grosse 
lie, Michigan. EPA/600R-04/167, 579 pp. 

Richards, R. P., D.B. Baker, J.W. Kramer, and D.E. 
Ewing. 1996. Annual Loads of Herbicides in 
Lake Erie Tributaries of Michigan and Ohio. J. 
Great Lakes Res., 22(2):414-428. 

Richardson, W.L., K.R. Rygwelski, X. Zhang, J.J. 
Pauer, and W. Melendez. 2001. Models of 
Chloride and Atrazine in Lake Michigan at Two 
Spatial Resolutions. Presented at the 44th 
Conference on Great Lakes Research, 
International Association for Great Lakes 
Research, University of Wisconsin, Green Bay, 
Wisconsin, June 10-14, 2001. 


129 







Rodgers, P.W. and D. Salisbury. 1981. Water 
Quality Modeling of Lake Michigan and 
Consideration of the Anomalous Ice Cover of 
1976-1977. J. Great Lakes Res., 7(4):467-480. 

Rygwelski, K.R., W.L. Richardson, and D.D. Endicott. 
1999. A Screening-Level Model Evaluation of 
Atrazine in the Lake Michigan Basin. J. Great 
Lakes Res., 25(1):94-106. ' 

Scholtz, M.T., B.J. Van Heyst, and A. Ivanoff. 1999. 
Documentation for the Gridded Hourly Atrazine 
Emissions Data Set for the Lake Michigan Mass 
Balance Study. U.S. Environmental Protection 
Agency, Office of Research and Development, 
National Exposure Research Laboratory, 
Research Triangle Park, North Carolina. EPA/ 
600/R-99/067, 61 pp. 

Schottler, S.P., S.J. Eisenreich, and P.D. Capel. 
1994. Atrazine, Alachlor, and Cyanazine in a 
Large Agricultural River System. Environ. Sci. 
Technol., 28(6):1079-1089. 

Schwab, D.J. and D. Beletsky. 1998. Lake Michigan 
Mass Balance Study: Hydrodynamic Modeling 
Project. National Oceanic and Atmospheric 
Administration, Great Lakes Environmental 
Research Laboratory, Ann Arbor, Michigan. 
NOAA Technical Memorandum ERL GLERL- 
108, 55 pp. 

Schwarzenbach, R.P., P.M. Gschwend, and D.M. 
Imboden. 1993. Environmental Organic 
Chemistry. John Wiley and Sons, Incorporated, 
New York, New York. 681 pp. 

Shipitalo, M.J. and L.B. Owens. 2003. Atrazine, 
Deethylatrazine, and Deisopropylatrazine in 
Surface Runoff from Conservation Tilled 
Watersheds. Environ. Sci. Technol., 37(5):944- 
950. 

Thomann, R.V. and D.M. Di Toro. 1975. 
Mathematical Modeling of Phytoplankton in Lake 
Ontario, Part 1 - Model Development and 
Verification. U.S. Environmental Protection 
Agency, Office of Research and Development, 
ERL-Corvallis, Large Lakes Research Station, 
Grosse lie, Michigan. EPA/600/3-75/005, 178 

pp. 


Thomann, R.V., R.P. Winfield, and J. Segna. 1979. 
Verification Analysis of Lake Ontario and 
Rochester Embayment Three-Dimensional 
Eutrophication Models. U.S. Environmental 
Protection Agency, Office of Research and 
Development, Environmental Research 
Laboratory, Duluth, Minnesota. 
EPA/600/3-79-094, 136 pp. 

U.S. Department of Agriculture. 2001. Agriculture 
Research Service Pesticide Properties Database. 
Available from U.S. Department of Agriculture at 
http://www.ars.usda.gov. 

U.S. Environmental Protection Agency. 2003a. 
Interim Reregistration Eligibility Decision (IRED) 
for Atrazine. U.S. Environmental Protection 
Agency, Office of Pesticides Program, 
Washington, D.C. Case Number 0062, 285 pp. 

U.S. Environmental Protection Agency. 2003b. 
Ambient Aquatic Life Water Quality for Atrazine - 
Revised Draft. U.S. Environmental Protection 
Agency, Office of Water, Washington, D.C. 
EPA/822\R-03\023,171 pp. 

Wanninkhoff, R.J. 1992. Relationship Between Gas 
Exchange and Wind Speed Over the Ocean. J. 
Geophys. Res., 97:7373-7381. 

Whitman, W.G. 1923. A Preliminary Experimental 
Confirmation of the Two-Film Theory of Gas 
Absorption. Chem. Metall. Eng., 29:146-148. 

Williams, R.J., D.N. Brooke, P. Matthiessen, M. Mills, 
A. Turnbull, and R.M. Harrison. 1995. Pesticide 
Transport to Surface Waters Within an 
Agricultural Catchment. J. Inst. Water Environ. 
Manag., 9(1 ):72-81. 


130 



PART 6 


REVIEW OF ATRAZINE MODELS 


Kenneth R. Rygwelski 

United States Environmental Protection Agency 
Office of Research and Development 
National Health and Environmental Effects Research Laboratory 
Mid-Continent Ecology Division 
Large Lakes and Rivers Forecasting Research Branch 

and 

Timothy J. Feist and Xiaomi Zhang 
Z-Tech, an ICF International Company 
Large Lakes Research Station 
9311 Groh Road 
Grosse lie, Michigan 48138 


6.1 LMMBP Atrazine Models 

6.1.1 Peer Reviews of LMMBP Atrazine 
Models 

Two modeling science peer reviews were conducted 
on the Lake Michigan Mass Balance Project 
(LMMBP) atrazine modeling products. These 
reviews were conducted near the beginning and final 
phases of the atrazine modeling work. The first 
review was general in nature and was conducted on 
June 23-25, 1998 in Southgate, Michigan and 
covered all components of the LMMBP modeling 
effort including project design and organization; 
project management, including an evaluation of 
resources; model linkages; sediment transport; 
loadings; hydrodynamics; model construct; atrazine; 
polychlorinated biphenyls (PCBs); eutrophication; 
and mercury. The second review was conducted on 
September27,2000 in Romulus, Michigan and solely 
focused on atrazine modeling. 


In the first review, panel members recommended that 
atrazine modeling advance to a level 2 type model 
(LM2-Atrazine) with more resolution than MICHTOX. 
Also, they recommended that management scenarios 
for the prediction of alternative futures include model 
sensitivity runs that include both zero atrazine 
concentrations in the vapor phase and non-zero 
concentrations, because measurements of the vapor 
phase concentrations in the basin were difficult to 
detect (see Part 4 for LM2-Atrazine modeling 
results). Reviewers encouraged the development 
and application of the high-resolution model, LM3- 
Atrazine (see Part 5 for results of LM3-Atrazine, a 5 
km x 5 km gridded model). The reviewers included 
United States Environmental Protection Agency 
(USEPA), Great Lakes National Program Office 
(GLNPO); Dr. Paul Capel, United States Geological 
Survey (USGS); Dr. Miriam Diamond, University of 
Toronto; Dr. Kevin Farley, Manhattan College; Dr. 
Raymond Hoff, Environment Canada; Dr. Robert 
Hudson, University of Illinois - Urbana Champaign; 
and Dr. Barry Lesht, Argonne National Laboratory. 




131 








Review comments from the second peer review 
appear in Appendix 6.1 of this Part. In general, 
comments received on atrazine modeling for the 
LMMBP were very favorable. Reviewers included 
USEPA/GLNPO; Dr. Paul Capel, USGS; and Dr. 
Robert Hudson, University of Illinois. 

The reviewers acknowledged that although the 
modified Stratified Beale Ratio Estimator (SBRE) 
method and the USGS ESTIMATOR used in the 
LMMBP are standard and reliable methods to 
estimate loadings, the length of the data record (one 
year) for the LMMBP was perhaps too short, and the 
number of samples taken from the tributaries to 
estimate loads was limited. Typically, multi-year 
records are used. There was follow-up discussion 
and evaluation of another load estimation procedure 
by Dr. Robert Hudson after the formal peer review 
comments were submitted. All of the necessary files 
were provided to Dr. Hudson to make loading 
assessments using rating curves similar to what is 
used in ESTIMATOR, but also to look at all the sites 
together rather than individually. He consulted 
USGS, who performed the LMMBP load estimates, 
before performing his analysis. The new attempt was 
not successful. The reviewers concluded that the 
load estimates made for the LMMBP using the 
watershed export percentage (WEP) approach were 
most likely the best estimates available for the 
project. 

It was recommended that further literature research 
be conducted to determine what type of degradation 
mechanisms may be operative in Lake Michigan. 
This was done and the results were reported in Part 
1, Chapter 2. 

The reviewers concluded that there are a number of 
combinations of watershed export percentages 
(WEPs) and in-situ decay rates that could achieve a 
model “fit” to the data. It is true that if the WEP were 
increased, the decay rate would have to increase. A 
concern was raised by the reviewers that these 
variables are somewhat unconstrained. However, 
the WEP was constrained by focusing only on 
northern freshwater drainage basins with soil texture 
similar to that of the Lake Michigan basin. Also, 
since rainfall can have an effect on measured WEPs, 
a balance of both wet and dry years were included in 
our long-term model runs. Using the mean WEP 
from these studies reported in the literature was the 


best estimate of the WEP’s central tendency in the 
Lake Michigan basin. Indeed, one of the reviewers, 
Paul Capel, looked at WEPs from 408 observations 
across numerous types of soil textures after the peer 
review and calculated a mean WEP of 0.66%, which 
was close to our mean of 0.6% (Capel and Larsen, 
2001 ). 

Other comments included a recommendation for a 
follow-up atrazine sampling of Lake Michigan water 
to help confirm short-term model predictions. This 
sampling was done in 2005; however, the results 
were not yet available at the time of this printing. 
Also, the reviewers suggested that a model 
sensitivity analysis be conducted. Sensitivity 
analyses were performed using both the MICHTOX 
model and the LM2-Atrazine models and are 
reported in Parts 3 and 4 of this report. 

The reviewers also were very pleased with the 
progress made with the LM3-Atrazine application and 
suggested that this high-resolution model would be 
very useful for making local environmental 
management decisions. The modelers agree with 
this assessment and have demonstrated local 
applications in the vicinity of the St. Joseph River, 
Fox River, Grand River, and the Kalamazoo River 
mouths. Some of the details of the St. Joseph 
application are discussed in Part 5. 

6.1.2 Comparison of LMMBP Models 

The LMMBP models are those discussed in this 
report: MICHTOX, LM2-Atrazine, and LM3-Atrazine 
(Part 3, 4, and 5, respectively). The differences in 
the model construct among these models has been 
discussed. Total annual atrazine loadings for all 
three models were the same and were based on an 
estimate of the 0.6% WEP. Both MICHTOX and 
LM2-Atrazine were calibrated using historical loading 
estimates and comparing model output to available 
lake data. Calibration consisted of selecting an 
appropriate in-situ total decay so that model output 
matched lake data. For Scenario 3, based on 
average conditions and the most likely scenario, 
MICHTOX yielded a half-life of atrazine in the lake of 
69.3 years (kinetic decay of 0.01/year). LM2-Toxic 
predicted a similar half-life of 77 years (kinetic decay 
of 0.009/year). LM3-Atrazine model used the 
0.009/yr decay derived from calibration of decay in 
the LM2-Atrazine model. 


132 



6.2 Comparison of LMMBP Models to 
Other Recent Atrazine Models Applied to 
Lake Michigan 

Within the last decade, three Lake Michigan atrazine 
modeling papers have been published. All three 
models were based on the principles of mass 
balance. However, the three models yielded very 
different estimates of in situ atrazine decay. 
Tributary loads carry the most atrazine to the lake 
compared to other sources. Therefore, any 
significant differences in the amount of atrazine 
delivered among the models will result in a range of 
internal decay estimates. There are many 
differences among these models, but the analysis 
here will specifically focus on the main reasons why 
these models differ. 

6.2.1 Schottler and Eisenreich (1997) 

Schottler and Eisenreich (1997) predicted an internal, 
overall, 14-year half-life for atrazine in Lake Michigan 
using a mass balance model called Stella. They 
used an atrazine WEP of 1% obtained from studies 
on basins outside of the Lake Michigan basin. 
However, their selection of WEP’s did not appear to 
be based on soil textures that match those of the 
Lake Michigan basin. Also, it was not clear if the 
WEP they used reflected wet or dry years (or a 
combination of both). These considerations could 
have an impact on selecting a representative WEP 
for the Lake Michigan basin (See Part 2, Chapter 2). 
The watersheds were from both northern and 
southern regions. The higher WEP used by Schottler 
and Eisenreich will yield higher atrazine tributary 
loads to be delivered to the lake (approximately 67% 
more mass loading from tributaries than the LMMBP 
models delivered) and therefore more internal decay 
was required in the lake to achieve a model fit to the 
lake data. Their model predicted that atrazine 
concentrations in the lake were at a steady-state 
concentration of 34 ng/L in 1994, but the model 
predicted that the lake concentration was close to 
this value since the late 1980s. The LMMBP models 
suggest that the lake, under constant 1995 loadings 
into the future, will reach a steady-state concentration 
of 66 ng/L in the year approximately 2194. 


6.2.2 Tierney et al. (1999) 

Tierney et al. (1999) predicted that the half-life of 
atrazine in Lake Michigan is about two years. The 
authors used atrazine run-off concentration data 
derived from the Lake Erie basin (Richards and 
Baker, 1993), and from Bodo (1991), who studied 
watersheds in Southwestern Ontario to make 
estimates of atrazine loading in the Lake Michigan 
basin. The soils in the Lake Erie basin have much 
more clay (Richards and Baker, 1993) than the soils 
in the Lake Michigan basin and run-off (WEP) of 
atrazine in the Lake Erie basin would likely approach 
percentages over 1% (see Table 2.2.2 in Part 2 of 
this report). The Lake Michigan basin has moderate 
textured soils, and the run-off WEP would be closer 
to 0.6%. Using atrazine concentration data from 
Lake Erie tributaries with high WEPs and applying 
them to characterize tributaries in the Lake Michigan 
basin would result in more atrazine loadings to Lake 
Michigan than what is likely, and therefore, in situ 
decay will need to be high in their model in order for 
the model to match observed lake data. High decay 
is associated with the short half-life that they report. 

Run-off loads of atrazine also is a strong function of 
the amount of atrazine applied to corn in the 
watershed. Predicted run-off concentrations in the 
Lake Michigan basin by Tierney et al. (1999) did not 
appear to be based on relating corn crop acreage in 
Lake Erie basin and Lake Michigan basin. They 
related flow-weighted concentrations in tributaries to 
% total agricultural land use and then applied them to 
the Lake Michigan basin. Total agricultural land use 
would be a poor predictor of atrazine 
usage/discharge if corn crop acreage per acre 
agricultural land varies within or between the Lake 
Erie and the Lake Michigan basins. The reason is 
that atrazine is used almost exclusively on corn crops 
in the Great Lakes basin. There is no indication in 
the paper that an analysis of corn crop acreage 
variation within agricultural lands was performed. To 
further complicate this issue, the amount of atrazine 
applied to corn acreage can vary from state-to-state. 

Loadings in their model (both from watershed run-off 
and precipitation) appear to be fixed to levels 
observed in the early 1990's and applied for the 
entire historical usage period of the chemical. This 
would have overestimated loads from the period 
leading up to approximately 1978. This 


133 








overestimation of loads in those early years would 
require that they include a significant non-zero 
atrazine decay term in their mass balance. 

I 

The Tierney model predicted that Lake Michigan 
reached steady-state atrazine concentrations in the 
mid- to late-1970s with a concentration of 33 ng/L. 

In contrast, Richardson and Endicott (1994) and 
Rygwelski et al. (1999) and the modeling work in this 
paper organized WEPs from the literature and used 
a WEP based on moderate textured soils typical of 
the Lake Michigan basin of 0.6%. Furthermore, 
Rygwelski et al. (1999) and this paper selected 
WEPs only from northern watersheds only and 
included a mix of both wet and dry years (see Part 2, 
Chapter 2). Also, only corn crops grown in the Lake 
Michigan basin were included in this analysis to 
determine atrazine loadings on a county-by-county 
basis. 

The results of the three atrazine models applied to 
Lake Michigan are displayed in Table 6.1. A WEP of 
approximately 5.6% was calculated for the Tierney 
model, based on their estimates of loads to the lake 
and amount of atrazine applied to the Lake Michigan 


watershed. It is clear from the table, that higher 
WEPs are associated with shorter atrazine half-lives. 

6.3 Atrazine Models Applied to Lake or 
Deep River Systems Outside the Lake 
Michigan Basin 

Other atrazine models have been applied to large 
freshwater lakes and rivers. Consistent with the 
results of the LMMBP models, these models have 
shown that little to no atrazine decays in these lakes 
and that loss via outflow from the lakes or rivers is 
the primary atrazine removal mechanism. 

6.3.1 Swiss Lakes 

Ulrich et al. (1994) modeled atrazine in an eutrophic 
lake, Greifensee, in Switzerland. The lake has a 
maximum depth of 32 m with a mean of 17.8 m. 
They found that, except for a short time in July and 
August, atrazine showed a somewhat conservative 
behavior. Within the overall mass balance, in situ 
decay accounted for only 5% of total annual loss of 
atrazine from the lake. Ninety-five percent of the loss 
from the lake was attributed to outflow. The authors 


Table 6.1 Comparison of LM2-Atrazine Model to Other Models 


Model 

WEP 

Half-Life 

Watershed Load 
Methods Used 

Estimated Year to 
Reach Steady- 
State 

Atrazine Steady- 
State Concentration 
ng/L 

Rygwelski and 
Zhang, 2007 
(Part 4 of this 
report) 

0.6% 

77 yrs. 

County 

Application and 
WEP 

2194 

66 

Schottler and 
Eisenreich, 

1997 

1.0% 

14 yrs. 

County 

Application and 

WEP 

1994 But 
Approached Near 
Steady-State 
Concentration in 
the Late-1980s. 

34 

Tierney et al., 
1999 

Not Used Directly 
(Approx. 5.6%) 

2 yrs. 

Run-off Flow and 
0.23 pg/L 

Forested; 1.6 pg/L 
Agricultural (Flow- 
Weighted) 

Mid- to Late-1970s 

33 


134 






noted that decay in the epilimnion layer of 0.003 per 
day was needed only in July and August to get the 
model to fit observations. They also noted that 
during that time, nitrate levels in the lake increased. 
High nitrate concentrations and high solar energy 
have been associated with indirect photolytic 
degradation of atrazine in water (see Section 1.2.3.2 
of this report). Since the lake stratified in the warm 
months of the year, water in the hypolimnion would 
be somewhat more isolated from photolytic decay 
than the epilimnion. During the rest of the year, 
atrazine was modeled without decay. Modeled 
processes such as volatilization and sedimentation 
were negligible. 

Buser (1990) modeled atrazine in Lake Zurich, 
Switzerland. The maximum depth of the lake is 136 
m with an average depth of 50 m. His results also 
showed atrazine to be rather stable and its removal 
primarily via outflowing waters compared to other 
loss processes such as sedimentation, degradation, 
and volatilization. This lake also stratified during the 
warm months of the year. 

Muller et al. (1997) modeled atrazine in three Swiss 
lakes: Greifensee, Murtensee, and Sempachersee. 
The maximum/mean depths for the Murtensee and 
Sempachersee are 45.5 m / 23.3 m and 87 m / 44 m, 
respectively. Except for the July and August period 
when they used an in situ decay of 0.003 per day in 
the epilimnion, atrazine was modeled as a 
conservative substance. Good agreement was 
achieved between model output and measured 
concentrations of atrazine in the lakes. 

6.3.2 St. Lawrence River 

Over an 18 month period in 1995 and 1996, Pham et 
al. (2000) measured the inputs and outputs of 
loadings of atrazine to a reach of the St. Lawrence 
River. The atrazine load was measured in both the 
upper part of the river near Cornwall, Ontario, 
Canada and at the outflow to the estuary, near 
Quebec City, Quebec, Canada. Taking into account 
loadings from the watershed, their measurements 
indicated that atrazine does not degrade during the 
three day transit in the river. This large river has a 
mean discharge of approximately 12,000 m 3 /s at 
Quebec City. At Cornwall, the depth is about 8.2 m 
and at Quebec City the depth is approximately 11 m. 


6.4 Atrazine Models Applied to Shallow 
Surface Water Systems in Agricultural 
Areas 

Atrazine degradation seems to be occurring in 
shallow surface water systems in agricultural areas. 
A hypothesis is that in these shallow systems, light 
energy penetrates a greater percentage of the water 
column than in lakes that show thermal stratification 
in the summer. Compared to these lakes, shallow 
rivers have fast mixing due to turbulence. This brings 
a fresh supply of atrazine close to the surface where 
photolysis can more easily degrade it. Rivers also 
generally have higher solids concentrations that 
could act as catalysts for hydrolysis. In deep lakes, 
summer stratification isolates water from photolysis 
in the hypolimnion and solids concentrations tend to 
be lower than that found in rivers. See Part 1, 
Chapter 2 for more discussion on this topic. 

6.4.1 Saylorville Reservoir, Iowa 

The Saylorville Reservoir is located on the upper Des 
Moines River basin in Northern Iowa near the city of 
Des Moines. Seventy-nine percent of the basin is 
cropland, mostly corn and soybeans. The reservoir 
is shallow, with a mean depth of only 4.3 m. Chung 
and Gu (2003) modeled atrazine transport and fate in 
1997. During the study period, the reservoir showed 
very weak thermal stratification in the summer 
months, which allowed them to assume well-mixed 
conditions. The authors found a strong inverse 
relationship between half-life and daily hours of 
sunlight. This supports the notion that photolysis was 
probably operative as a loss mechanism. In this 
system, approximately 60% of the atrazine that 
entered the reservoir was released through 
discharge. Approximately 40% of atrazine in the 
reservoir was transformed via kinetic loss 
mechanism(s) such as photolysis, hydrolysis, etc. 
The half-life of atrazine in the reservoir varied from 
two to 58 days. Their analysis found that the half-life 
of atrazine did not correlate well with nitrate 
concentrations, suggesting that photolysis was not 
nitrate-mediated indirect photolysis. Rather, they 
indicated that direct photolysis, aided by the high 
concentrations of dissolved organic carbon (DOC), 
was probably operative. 


135 








6.4.2 Other Small Surface Water Systems 

Other modeling studies in small lakes and a shallow 
creek in agricultural regions have shown similar, 
relatively short half-lives of atrazine. 

Spalding et al. (1994) estimated the atrazine half- 
lives in two very small lakes in Northeastern 
Nebraska ranged from 124 to 193 days. Spalding 
suggested that hydrolysis may have been 
responsible for degradation of atrazine in these 
lakes. However, these lakes had relatively high pH’s 
averaging 8.1 for one lake and 8.2 for the other, and 
some researchers have found that hydrolysis above 
pH 4 was difficult to achieve in the laboratory. The 
authors did not rule out photolytic decay. These 
small lakes were very turbid where average Secchi 
readings were less than 1 m. None of the other 
atrazine modeling papers reviewed suggested 
hydrolysis as a possible explanation of atrazine 
decay. 

In a small creek in Iowa, Kolpin and Kalkhoff (1993) 
found that atrazine half-lives had a significant inverse 
relationship with sunlight, therefore suggesting 
photolysis was responsible. To rule out temperature 
as a confounding variable, they found that comparing 
atrazine half-lives to water temperature did not yield 
a significant correlation. The degradation in Roberts 
Creek was very rapid. Half-lives ranged from 168 
hours (November 1990) to 35.6 hours (July 1990). 
The stream discharge ranged from 0.264 to 0.086 
m 3 /s during the sampling period, April 11, 1990 to 
December 2, 1990. 

6.5 Conclusions 

A likely reason for the discrepancies (see Table 6.1) 
between the three Lake Michigan atrazine modeling 
efforts has to do with the wide range of estimates 
made for tributary loadings of atrazine to the lake. 
Since tributary loadings are the major source of 
atrazine to the lake, rigorous detailed efforts need to 
be taken to ensure that these loads are as accurate 
as possible. 

Atrazine decay in large surface water systems (lakes 
and rivers) appears to be much lower than decay 
found in shallow water systems. In larger systems, 
decay is very slow with half-lives estimated in years. 
In shallow, small systems with a high degree of 


mixing, atrazine decay can be rapid with half-lives 
estimated in days or even hours. 

Research suggests that decay in surface water may 
be linked to photolysis, either direct of indirect. 
Modeling studies in deeper lakes suggest that this 
happens in the summertime when solar energy is 
high. Photolysis is limited in lakes that are stratified 
or deep rivers, because the exposure of light energy 
to the inventory of atrazine in these systems is 
limited. Systems that are well-mixed further facilitate 
photodegradation, because a fresh supply of atrazine 
is constantly being brought to the water surface 
where light energy would be the greatest. Atrazine in 
a hypolimnion layer would be less available for 
photolysis because it is somewhat isolated from the 
mixed epilimnion layer due to the thermocline. 

In regards to Lake Michigan, can other degradation 
processes besides photodegradation explain the in 
situ decay? Per Part 1, Chapter 2, Section 1.2.3.1, 
biodegradation in surface waters is not likely. 
Hydrolysis in Lake Michigan is not likely because of 
the high pH of 8.4, low solids, and low DOC (see Part 
1, Chapter 2, Section 1.2.3.2.1). 

References 

Bodo, B.A. 1991. Trend Analysis and Mass- 
Discharge Estimation of Atrazine in Southwestern 
Ontario Great Lakes Tributaries: 1981-1989. 
Environ. Toxicol. Chem., 10(9): 1105-1121. 

Buser, H.-R. 1990. Atrazine and Other s-Triazine 
Herbicides in Lakes and in Rain in Switzerland. 
Environ. Sci. Technol., 24(7):1049-1058. 

Capel, P.D. and S.J. Larson. 2001. Effect of Scale 
on the Behavior of Atrazine in Surface Waters. 
Environ. Sci. Technol., 35(4):648:657. 

Chung, S. and R.R. Gu. 2003. Estimating Time- 
Variable Transformation Rate of Atrazine in a 
Reservoir. Adv. Environ. Res., 7(4):933-947. 

Kolpin, D.W. and S.J. Kalkhoff. 1993. Atrazine 
Degradation in a Small Stream in Iowa. Environ. 
Sci. Technol., 27(1 ):134-139. 


136 



Muller, S.R., M. Berg, M.M. Ulrich, and R.P. 
Schwarzenbach. 1997. Atrazine and Its Primary 
Metabolites in Swiss Lakes: Input Characteristics 
and Long-Term Behavior in the Water Column. 
Environ. Sci. Technol., 31 (7):2104-2113. 

Pham, T.-T., B. Rondeau, H. Sabik, S. Prouix, and D. 
Cossa. 2000. Lake Ontario: The Predominant 
Source of Triazine Herbicides in the St. Lawrence 
River. Can. J. Fisher. Aquat. Sci., 57(Suppl. 
1 ):78-85. 

Richards, R.P. and D.B. Baker. 1993. Pesticide 
Concentration Patterns in Agricultural Drainage 
Networks in the Lake Erie Basin. Environ. 
Toxicol. Chem., 12(1 ):13-26. 

Richardson, W.L. and D.D. Endicott. 1994. A 
Screening Modelfor Establishing Load-Response 
Relationships for Toxic Chemicals in Lake 
Michigan. Presented at the Fifteenth Annual 
Meeting of the Society of Environmental 
Toxicology and Chemistry (SETAC), Denver, 
Colorado, October 30 - November 3, 1994. 

Rygwelski, K.R., W.L. Richardson, and D.D. Endicott. 
1999. A Screening-Level Model Evaluation of 
Atrazine in the Lake Michigan Basin. J. Great 
Lakes Res., 25(1 ):94-106. ' 


Schottler, S.P. and S.J. Eisenreich. 1997. Mass 
Balance Model to Quantify Atrazine Sources, 
Transformation Rates, and Trends in the Great 
Lakes. Environ. Sci. Technol., 31 (9):2616-2625. 

Spalding, R.F., D.D. Snow, D.A. Cassada, and M.E. 
Burbach. 1994. Study of Pesticide Occurrence 
in Two Closely Spaced Lakes in Northeastern 
Nebraska. J. Environ. Qual., 23(3):571-578. 

Tierney, D.P., P.A. Nelson, B.R. Christensen, and 
S.M. Kloibery Watson. 1999. Predicted Atrazine 
Concentrations in the Great Lakes: Implications 
for Biological Effects. J. Great Lakes Res., 
25(3):455-467. 

Ulrich, M.M., S.R. Muller, H.P. Singer, D.M. 
Imboden, and R.P. Schwarzenbach. 1994. Input 
and Dynamic Behavior of the Organic Pollutants 
Tetrachloroethylene, Atrazine, and NTA in a 
Lake: A Study Combining Mathematical 

Modeling and Field Measurements. Environ. Sci. 
Technol., 28(9):1674-1685. 


137 





PART 6 


REVIEW OF ATRAZINE MODELS 


Appendix 6.1 Peer Review of LMMBP 
Atrazine Models, September 27, 2000, 
Romulus, Michigan 

Report of the Second Review Panel Meeting 
Submitted to: 

Dr. Glenn Warren 

United States Environmental Protection Agency 

Great Lakes National Program Office 

77 W. Jackson Boulevard 

Chicago, Illinois 60606-3590 

by 

Paul Capel 

United States Geological Survey 
Water Science Center of Minnesota 
Mounds View, Minnesota 55112 
and 

Robert Hudson 
University of Illinois 
S-518 Turner Hall, MC-047 
1102 South Goodwin Avenue 
Urbana, Illinois 61801 

A.6.1.1 Overview 

The second review meeting was focused solely on 
the work that the Lake Michigan Mass Balance 
Program (LMMBP) has completed on atrazine. 
Presentations were made on the following subjects: 
data quality assurance; summary statistics 
measurements in air, rain, tributaries, and lake water; 
tributary load calculations; modeling atmospheric 
transport and deposition; atmospheric deposition 
calculation results; hydrodynamic transport in the 41 
segment model; hind/forecasting using MICHTOX; 


and, simulation results from 41-segment and high- 
resolution models. 

The review team feels that the LMMBP has generally 
met its goals for modeling atrazine loading to and 
fate and transport within Lake Michigan. The 
following aspects of the work were notably strong: 

A. The data management system and data quality 
assurance program were excellent. A great deal 
of work was expended to develop the platforms 
and communication that was needed to make 
such a large data set useful. This work had 
recently undergone an independent review. 

B. The atmospheric modeling (from volatilization to 
deposition) is an important contribution, both to 
the LMMBP and to the scientific community. This 
is the first attempt at a regional model for a semi¬ 
volatile chemical. Although the work is still 
underway, the planned attempts to compare 
model predictions with the field measurements is 
commended. 

C. The hydrodynamic components of the 41- 
segment model appear to be complete and well- 
calibrated, based on the results for temperature 
and chloride. These components of the model 
will be further tested when the focus shifts from 
atrazine, which is largely dissolved, to the 
particle-associated chemicals (mercury and 
PCBs). 

D. The simulations of atrazine fate within the lake 
based on the MICHTOX, 41-segment, and high- 
resolution models agreed well with each other 


138 



and with the measured data. The fact that the 
measured atrazine concentrations were relatively 
homogeneous throughout the lake (22 to 58 jjg/L) 
made the comparison of measured and modeled 
results a “relatively” straightforward, albeit 
necessary test of the model. 

E. The use of MICHTOX (Rygwelski et at., 1999) to 
simulate the evolution of atrazine levels in the 
lake since atrazine use began (hindcasting) and 
to forecast future levels was an excellent way to 
tackle the issue of the rate of atrazine decay 
within the lake. It also plays an important role in 
testing the consistency of the loading and decay 
estimates. 

F. The high-resolution modeling has significance far 
beyond the potential improvements in scientific 
understanding of atrazine fate it may bring. The 
model should be of great use in making local 
environmental management decisions. In 
addition, the animations produced from the daily 
simulations should serve as an excellent 
communications tool for environmental managers 
to reach the public with. This work should 
continue to be strongly encouraged. 

A.6.1.2. Comments on Technical Issues 

A. Tributary Loads of Atrazine - The LMMBP 
work to date indicates that about 2/3 of the 
atrazine load to Lake Michigan is borne by rivers. 
The tributary loads were estimated using various 
statistical approaches, such as the modified 
Beale method and the USGS ESTIMATOR 
software, to derive loads from a limited number of 
dissolved atrazine measurements in water from 
the rivers in the Lake Michigan basin. Although 
determining “true” loads is impossible, these 
estimation methods have proved reliable and are 
considered standard where non-point source 
loads need to be quantified. In this case, 
however, the length of the data record for each 
tributary (one-year) is short with a limited number 
of measurements in comparison to multi-year 
records that are typically used. Therefore, the 
reviewers suggest exploring other statistical 
approaches that can be used on the existing data 
set. Appendix 1 (in preparation) presents a brief 
description of one such approach that could be 


considered. [Note to readers: Appendix 1 was 
not completed by the review panel. However, Dr. 
Hudson did make some loading estimates using 
rating curves similar to what is used in 
ESTIMATOR, but looked at all of the sites 
together, rather than individually. He consulted 
USGS, who performed the LMMBP load 
estimates, before performing his analysis. The 
new attempt was not successful.] 

The tributary loads were also estimated using the 
“watershed export percentage” (WEP) approach 
and the estimated annual use of atrazine in each 
watershed of rivers flowing into the lake. This 
approximate method serves as a good check on 
the tributary load calculations and has the benefit 
of allowing the tributary loads to easily be 
estimated each year for the hind/forecasting. 

B. Atmospheric Deposition - The magnitude of 
atmospheric deposition was estimated through 
field measurements (for rain) and simple models 
(for dry deposition). It appears that inputs via 
rain are dominant. A single, typical value for the 
particle depositional velocity was chosen and all 
of the estimates based on this single value. The 
reviewers suggest that the model sensitivity to 
this approximation should be examined by 
choosing an appropriate range of particle 
depositional velocities. Large particles, coming 
from Chicago, have been shown to have much 
higher depositional velocities than the “typical” 
value used, although it is unknown how much 
atrazine is on these larger particles. [Note to 
readers: Only wet deposition was estimated for 
the Lake Michigan atrazine models, because dry 
deposition was negligible. See Part 1, Chapter 3 
for more information.] 

C. Atrazine Decay Processes - Atrazine was 
initially selected for study in the LMMBP as a 
model of a reactive, biodegradable compound 
(see Section 1.1 of Statistical Assessment of QA 
Data documents). A half-life of 14 years was 
estimated by Schottler and Eisenreich (1997) 
based on the assumption that atrazine should be 
approximately at steady-state within the lake. 
Rygwelski et al. (1999) showed that current 
atrazine levels within the lake could be predicted 
from plausible historical loading estimates 


139 







assuming no decay of atrazine within the lake. 
This approach leads to predictions of very large, 
continued increases in lake atrazine levels. 
Although atrazine levels are not likely to exceed 
current drinking water standards, this scenario is 
obviously of greater concern than the steady- 
state assumption. 

Further literature review of mechanisms of 
atrazine decomposition is warranted to help 
determine which mechanisms are most likely to 
be significant in the lake. Given the current state 
of knowledge, it may be difficult to resolve this 
issue. However, the full range of processes - 
biodegradation, photochemical decomposition, 
and chemical hydrolysis should be considered. 
The possibility of more significant decay within 
the lake needs to be kept open. 

D. Summary - Both the data and modeling results 
suggest that atrazine may not be as reactive 
within the lake as originally anticipated. This 
question is probably best resolved by continued 
monitoring of atrazine levels in lake water. The 
model results can be used to ensure that 
sampling locations are not unduly affected by 
tributary inputs. Further modeling work in this 
area should combine the historical approach with 
parameter sensitivity analyses. The results 
presented in the review meeting showed that the 
measured data can be correctly modeled by 
different combinations of WEP and atrazine’s 


degradation rate in Lake Michigan. At this time, 
neither parameter is well-constrained. It is 
suggested by the panel that the LMMBP 
investigate the relationship between values of 
WEP and degradation rate that yield accurate 
estimates of current atrazine levels from historical 
loading rates. Presumably, an inverse 
relationship between the two will result, with an 
acceptable range for each. 

The above discussion concerns an example of 
variables in the models that are constrained at 
this time only to a range of values, rather than a 
single correct value. The LMMBP might wish to 
consider other model variables to evaluate the 
model’s sensitivity to the appropriate ranges of 
these values and to the relationships between 
parameters. 

References 

Rygwelski, K.R., W.L. Richardson, and D.D. Endicott. 
1999. A Screening-Level Model Evaluation of 
Atrazine in the Lake Michigan Basin. J. Great 
Lakes Res., 25(1 ):94-106. ' 

Schottler, S.P. and S.J. Eisenreich. 1997. Mass 
Balance Model to Quantify Atrazine Sources, 
Transformation Rates, and Trends in the Great 
Lakes. Environ. Sci. Technol., 31 (9):2616-2625. 


140 




























































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